http://informahealthcare.com/rnf ISSN: 0886-022X (print), 1525-6049 (electronic) Ren Fail, 2014; 36(7): 1060–1066 ! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/0886022X.2014.918814
CLINICAL STUDY
A comparison of markers of myocardial injury and their relation to nutritional parameters in hemodialysis patients Milan D. Stosovic1, Sanja Dj. Stankovic2, Mirjana Lj. Stanojevic1, Sanja P. Simic-Ogrizovic1, Dijana B. Jovanovic1, and Radomir T. Naumovic1 Clinic of Nephrology, Clinical Center of Serbia, Belgrade, Serbia and 2Center for Medical Biochemistry, Clinical Center of Serbia, Belgrade, Serbia
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1
Abstract
Keywords
Background: Serum cardiac troponin T (cTnT) is a valuable marker of ischemic heart disease (IHD) and left ventricular hypertrophy, as well as a mortality predictor in hemodialysis populations. We compared the value of cTnT, creatinine kinase (CK)-MB mass and myoglobin as mortality predictors in our hemodialysis patients and evaluated their relation to nutritional status. Methods: A total of 118 hemodialysis patients were prospectively studied from January 2004 to April 2013. Clinical and laboratory evaluations during the 12-month baseline period included the history of IHD, signs of left ventricular hypertrophy (LVH), Kt/V and serum cardiac markers together with the percentage of body fat (%fat), mid-arm circumference (MAC), mid-arm muscle circumference (MAMC), triceps skinfold (TSF) and BMI. Results: Underweight patients had significantly higher cTnT values (Mann–Whitney, p50.05). Correlation analysis (Spearman) showed an inverse association between cTnT and TSF ( ¼ 0.22, p50.05), as well as between CK-MB mass and TSF ( ¼ 0.26, p50.01). In men cTnT also correlated inversely with %fat ( ¼ 0.27, p50.05) and BMI ( ¼ 0.33, p50.05). In addition, myoglobin was correlated significantly with MAC, MAMC and albumin. Among cardiac markers cTnT was the only independent variable predicting mortality (Multivariate Cox regression, HR ¼ 1.04 CI (1.01–1.07); p50.01; measurement units 0.01 mg/L). Conclusion: Troponin T and CK-MB mass were significantly elevated in the underweight patient group. Troponin T was the only independent cardiac marker predictor of all cause mortality in our hemodialysis patients.
Anthropometry, cardiac markers, hemodialysis, nutrition
Introduction The prevalence of ischemic heart disease in hemodialysis patients is 10–20 times higher than that in the general population, with 50% mortality due to cardiovascular disease.1 In non-uremic patients with suspected myocardial damage determination of myoglobin, creatinine kinase MB (CK-MB) and troponins has been shown to be reliable for early diagnosis of myocardial damage and risk stratifications, as well as in stable coronary artery disease patients.2–4 However, in hemodialysis patients these markers are elevated and diagnosis of myocardial injury based on them is less reliable.5 In addition, reasons for their elevation are complex and due rather to chronic cell injury than to decreased clearance.5,6 Although other cardiac markers, such as BNP (or NT-proBNP and proBNP), are also very powerful biomarkers, at least for left ventricular hypertrophy diagnostics and mortality prediction in hemodialysis patients, they are not used for myocardial ischemic injury diagnosis. Another problem related to cardiovascular disease in this Address correspondence to Milan Stosovic, Clinic of Nephrology, Clinical Center of Serbia, Pasterova 2, Belgrade 11000, Serbia. Tel/Fax: +38 111657944; E-mail: [email protected]
History Received 23 January 2014 Revised 5 March 2014 Accepted 24 April 2014 Published online 19 May 2014
population is reverse epidemiology, as obese patients survive for longer than others.7,8 As expected, obese patients with non ST segment elevation myocardial infarction in the non-kidney disease population have a raised level of troponin T.9 The question arises about the relation of the traditional risk factor, obesity, and standard markers of myocardial injury in hemodialysis patients. Therefore the present 9-year prospective study compared mortality prediction of markers of myocardial injury and evaluated their relation to nutritional status.
Patients and methods This prospective observational study included a cohort of 118 patients (58 men) on maintenance hemodialysis in the Dialysis Unit of the Clinical Center of Serbia. The design of the study was approved by the Ethics Committee of the Clinical Center. All patients were monitored until their death (56 patients), kidney transplantation (7 patients), departure from the center (11 patients) or the end of the study on April 30, 2013 (24 men and 20 women). Baseline measurements were made for 12 months from 1st January to 31st December 2004, when the patients entered the
Cardiac markers and nutrition in HD patients
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DOI: 10.3109/0886022X.2014.918814
study and involved clinical, anthropometrical (February) and monthly laboratory examinations. After that the participants were monitored continually. Besides clinical monitoring and laboratory testing, anthropometric measurements were done every year until end of the study. The patients were dialyzed three times per week for 3–5 hours using bicarbonate solutions. The dialyzers were never reused. A standard diet was prescribed for hemodialysis patients10 that did not change during the observation period. Predialysis blood pressure was measured at the beginning of every dialysis session. Predialysis mean arterial pressure (MAP) was calculated from the formula [MAP ¼ diastolic + (systolic diastolic)/3]. Patients with a history or signs of ischemic heart disease (IHD) and left ventricular hypertrophy (echocardiography and electrocardiography, LVH) were considered to have this co-morbidity. Overall comorbidity was assessed using the index of physical impairment (IPI), which has three Physical Impairment categories: 0 – Normal function; 1 – Mild/moderate impairment; 2 – Serious/severe impairment.11 Fat percentage (%fat) was determined from the sum of triceps (TSF), biceps, suprailiac and subscapular skinfolds, as recommended by the DOQI Clinical Practice Guidelines.12 Normal intervals were 12–20% for men and 20–30% for women.13 The BMI normal interval was 18.6–24 kg/m2.14 Mid-arm muscle circumference (MAMC) was calculated from mid-arm circumference (MAC) from the formula [MAMC (cm) ¼ MAC (cm) 0.314 TSF (mm)]. Kt/V was obtained using the second generation Daugirdas formula.15 Kt=V ¼ lnðR 0:008 tÞ þ ð4 3:5RÞ UF=W where R ¼ post-dialysis/pre-dialysis blood urea nitrogen, t ¼ dialysis hours, UF ¼ pre-post dialysis weight change and W ¼ post-dialysis weight. Before the second dialysis/week blood samples were taken into Vacutainer tubes (BD Vacutainer Systems, Franklin Lakes, NJ). Blood samples for cardiac markers were collected during February 2004; one sample from each patient at the same time as the anthropometric measurements were made. They were not taken if there was evidence of stenocardia or an acute coronary event in the preceding 2 weeks, so the cardiac markers levels represent chronic elevations. Serum troponin I (cTnI), the MB fraction of creatine kinase and myoglobin concentrations were measured using microparticle enzyme immunoassays on an AxSYM System (Abbott Diagnostics, Wiesbaden, Germany). Serum troponin T was measured by an electrochemiluminescence immunoassay on a Roche Elecsys 2010 automated analyzer (Roche Diagnostics, Mannheim, Germany). Serum urea, creatinine, calcium, phosphate and hemoglobin levels were determined using routine clinical assays. All the following assays were validated in our laboratory. For the Roche Troponin T assay the limit of detection was 0.01 mg/L, with intra- and inter-assay coefficients of variation (CV) between 1.1% and 2.3%. For the AxSYM Troponin-I ADV assay 0.02 mg/L was the limit of detection with intra- and inter-assay CV between 4.3% and 9.4%. For the AxSYM CK-MB assay the limit of detection was 0.7 mg/L, with intra- and inter-assay CV between 1.2% and 7.0%. For the AxSYM Myoglobin assay limit of detection
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was 51.0 mg/L, with intra- and inter-assay CV between 3.0% and 5.1%. While 94 patients had measurable levels of cTnT only 16 patients had measurable levels of cTnI and they were high (from 0.10 to 8.90 mg/L). Therefore cTnI was omitted from further analysis. Statistical methods Predictor variables used in the Cox regression were derived from data in the baseline period. Differences between the groups were estimated using the t-test or Mann–Whitney test. Chi square with Yates’s correction and Fisher exact test were used for frequencies. Pearson or Spearman correlation coefficients were employed to detect associations among predictor variables. The parameters were tested in the baseline period. The cut-off point for statistical significance was 0.05. In order to determine collinearity for survival analysis correlation testing revealed marked associations among the cardiac markers. cTnT and CK-MB mass are potentially collinear (Spearman, ¼ 0.58, p50.01 and myoglobin with CK-MB mass (Spearman, ¼ 0.28, p50.01). Correlations among the anthropometric parameters were strong. Body fat percentage was collinear (Pearson, r40.5, p50.01) with TSF, MAC and BMI. MAC was collinear with %fat, TSF, MAMC, BMI and BW. BMI was collinear with %fat, MAC, MAMC, TSF and BW. The effect of each factor on patient survival was examined by univariate survival analysis. The Cox proportional hazard model was employed using time-independent covariates. Proportional hazards were tested by fitting time-varying models. The outcome was death and all the other patients were censored (transplanted, left the center, alive at the study end on April 30, 2013). Survival time was measured in months (from the beginning of the study until death or censoring). All variables were tested as potential predictors of death in the univariate analysis, including demographic, primary kidney disease data, time in dialysis, laboratory and clinical data, as well as cardiac markers and anthropometric parameters, in order to adjust the analysis for these variables. Variables that were potential predictors of death in univariate analysis (p50.10) were tested in the multivariate Cox proportional hazard model using the time-independent covariates, forward stepwise method. In each model the number of variables was at least ten times less than the number of patients.
Results Demographic data and primary kidney disease for all patients at the time of their inclusion in the study are presented in Table 1. Glomerulonephritis was the most frequent disease, while hypertension and diabetes together were responsible for end stage renal disease in 15.2% of the patients. Laboratory, clinical data and cardiac markers are shown in Table 2. Nutritional variables determined in the same period are presented in Table 3 separately for men and women. Using FAO (Food and Agriculture Organization) normal intervals for %fat, the patients were divided into underweight, normal weight and overweight. Among our patients 15.5% men and
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Table 1. Demographic variables and diagnosis. Parameter
Value
Sex Men Women Age (years)1 Time in dialysis (months)2 Diagnosis3 Chronic glomerulonephritis Hypertension (nephrosclerosis) Polycystic kidney disease Chronic interstitial nephropathy4 Diabetes Systemic diseases Other Unknown
58 60 55.0 (13.3) 64 (35 to 126) 16.9 8.5 11.0 11.9 6.7 5.9 17.8 21.2
(20) (10) (13) (14) (8) (7) (21) (25)
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1 – Mean (SD); 2 – median (interquaritle value); 3 – percent (count); 4 – Chronic interstitial, nephropathy includes Balkan endemic nephropathy, Chronic interstitial nephritis and Chronic pyelonephritis.
Figure 1. Underweight patients had higher serum levels of troponin T and creatinine kinase MB mass.
Table 2. Laboratory, clinical data and cardiac markers. Parameter
Mean (SD)
Normal values
Kt/V1 1.36 MAP (mmHg)1 95 IHD3 33.9 3 LVH 30.5 IPI2 0 Hemoglobin (g/L)1 100.1 Urea (mmol/L) 25.8 Creatinine (mmol/L)1 895 Calcium (mmol/L)1 2.31 Phosphorus (mmol/L)1 1.61 cTnT (mg/L) 2 0.038 CK-MBmass (mg/L)2 2.10 Myoglobin (mg/L)2 176
(0.24) 41.2 (10) 5100 (40) – (36) – (0 to 1) 0* (15.2) M: 138–175 W: 119–157 (3.7) 2.5–7.5 (167) M: 53–124 W: 53–106 (0.15) 2.10–2.70 (0.35) 0.80–1.60 (0.012 to 0.104) 50.01 (1.35 to 3.10) 3.8 (139 to 263) 5116.3
1 – Mean (SD); 2 – median (interquaritle value); 3 – percent (count); *It is normal to have zero comorbidity, range (0–3); Normal values of laboratory analysis are for health persons not for hemodialysis patients; MAP – mean arterial pressure; IHD – ischemic heart disease; LVH – left ventricular hypertrophy; IPI – index of physical impairment; cTnT – troponin T; CK-MBmass – creatinine kinase MB mass; M – men; W – women.
Table 3. Nutritional variables. Parameter Body weight (kg) BMI (kg/cm2) Percentage of body fat Mid-arm circumference (cm) Triceps skinfold (mm) Mid-arm muscle circumference (cm) Albumin (g/L)
Men 71.1 23.4 21.0 28.4
(13.1) (3.7) (7.7) (4.4)
Women 57.0 22.1 28.6 26.6
Normal values
(10.2) – (3.5) 18.6–24.9 (7.4) M: 12–20; F: 20–30 (4.2) –
11.2 (5.9) 24.9 (3.0)
15.7 (7.3) 21.7 (2.5)
– –
41.3 (3.7)
40.8 (3.2)
34–55
All variables were expressed as mean (SD).
15.2% women were found to be underweight, while 55.1% men and 42.4% women were overweight. IPI score was 0 in 70 patients, 1 in 40 patients and 2 in 8 patients. Ischemic heart disease was found in 32.7% of the men and 35% of the women.
Testing differences among the variables when patients were grouped by sex showed that men had a significantly lower Kt/V index than women (t-test, p50.01), but there were no significant differences in age, time in dialysis and comorbidity. The group of patients with IHD had a significantly higher level of cTnT (Mann–Whitney, p50.01), CK-MB mass (Mann–Whitney, p50.01), but lower concentrations of myoglobin (Mann–Whitney, p50.01). There was no significant difference in time in dialysis between the IHD groups (Mann– Whitney). In these patients creatinine was markedly lower than in patients without IHD (t-test, p50.01) and they were older (t-test, p ¼ 0.01). Kt/V was significantly lower in the IHD group (mean value 1.27, t-test, p50.01). In addition IPI was higher (Chi-square, p50.01) and hypertension (Fisher, p50.05) and diabetes (Fisher, p50.01) were more frequent at primary diagnosis in IHD patients. The group of patients with LVH showed a significantly higher CK-MB mass (Mann–Whitney, p50.05), lower Kt/V (t-test, p50.01), higher MAP (t-test, p50.01) and lower concentrations of serum phosphate (t-test, p50.05). This group of subjects had a higher IPI score (Chi-square, p50.05) and diabetes more frequently at primary diagnosis (Fisher, p50.01). No significant difference in time in dialysis between the LVH groups (Mann–Whitney) was found. The group of underweight patients had significantly higher cTnT (Mann–Whitney, p50.05) and CK-MB mass (Mann– Whitney, p50.05) Figure 1. However, underweight patients were longer on dialysis (median 105 months) than normal and overweight subjects (median 58 months; Mann–Whitney, p50.05). No other variables were significant in this group of patients. In overweight patients cTnT levels were somewhat lower but the difference was not significant when compared with normal weight and underweight individuals. Correlation analysis between cardiac markers and nutritional parameters revealed an inverse association between cTnT and TSF (Spearman, ¼ 0.22, p50.05), as well as between CK-MB mass and TSF (Spearman, ¼ 0.26, p50.01). In men cTnT correlated inversely with %fat (Spearman, ¼ 0.27, p50.05), TSF (Spearman,
DOI: 10.3109/0886022X.2014.918814
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¼ 0.37, p50.01) and BMI (Spearman, ¼ 0.33, p50.05). In addition, myoglobin was significantly correlated with MAC (Spearman, ¼ 0.24, p50.01), MAMC (Spearman, ¼ 0.26, p ¼ 0.01) and albumin (Spearman, ¼ 0.23, p50.05). Correlations among cardiac markers and clinical and laboratory variables were also significant – cTnT and Kt/V (Spearman, ¼ 0.25, p50.01), CK-MB mass and Kt/V (Spearman, ¼ 0.25, p50.01), myoglobin and Kt/V (Spearman, ¼ 0.20, p50.05). In addition cTnT correlated significantly with phosphate (Spearman, ¼ 0.21, p50.05). Because our study included prevalent patients a correlation analysis (Spearman) was conducted for time in dialysis with all continuous variables. No significant correlation was found for cardiac markers. However, time in dialysis was associated with some anthropometric parameters (MAC ¼ 0.24, p50.01; MAMC ¼ 0.21, p50.05 and TSF ¼ 0.18,
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p50.05), as well as with albumin ( ¼ 0.22, p50.05), hemoglobin ( ¼ 0.33, p50.01), urea ( ¼ 0.18, p50.05) and creatinine ( ¼ 0.25, p50.01). Further exploration of the relation between cTnT and anthropometric parameters revealed that it was nonlinear but after transformation of cTnT (natural logarithm) it became linear. Figure 2 shows linear regression between % body fat and ln(cTnT), and TSF and ln(cTnT), where the relation between ln(cTnT) and anthropometric parameters is inverse and significant only for %fat and TSF. Unfortunately, during the 9-year period 56 patients (47.4%) died. Among them there were 26 men (46.4%) and 30 women (53.6%). ROC analysis for morality prediction was carried out for cardiac markers (Figure 3). Among them, area under curve (AUC) was largest for cTnT (AUC 0.65; 95% CI 0.55 to 0.75; p50.01) and also significant for CK-MB mass (AUC 0.64; 95% CI 0.54 to 0.74; p50.01). Among the anthropometric
Figure 2. Linear regression analysis for troponin T and anthropometric parameters (percentage of body fat and triceps skinfold).
Figure 3. Receiver operating characteristic analysis for morality prediction (for cardiac markers and anthropometric parameters).
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parameters (Figure 3), MAMC and MAC had similar values (AUC 0.36; 95% CI 0.26 to 0.46; p50.01). Other anthropometric parameters did not have a significant AUC. The curves of TSF and BMI in the ROC graph were between the MAMC curve and the reference line. Potential predictors of all-cause death were selected using the univariate Cox proportional hazard model (p50.10; Table 4). Among cardiac markers cTnT and CKMB mass were potential predictors of all cause mortality in these patients. The variable cTnT was multiplied by 100 for clearer presentation of the results. Potential predictors of mortality among the nutritional parameters were MAC, MAMC and albumin. Although this was the prevalent patient group, time in dialysis was not selected as a potential predictor of death. Among clinical and laboratory variables age, nephrosclerosis, diabetes mellitus, IHD, LVH, IPI, Kt/V and creatinine were selected by univariate analysis. However, diabetes mellitus was not proportional. As a rule of thumb, according to the number of patients only eleven variables were selected for multivariate analysis: age, nephrosclerosis, IHD, LVH, IPI, Kt/V, creatinine, albumin, MAMC, cTnT100 and CK-MB mass. MAC was collinear with MAMC. Since cTnT100 and CK-MB mass were collinear, three models were tested: cTnT100, CK-MB mass model and a model with both variables. The individual predictive power of each variable was evaluated using multivariate Cox regression. The results are presented in Table 5. In the cTnT model, age, ischemic heart disease, left ventricular hypertrophy and albumin were independent variables together with cTnT. Proportional hazard analysis indicated a 4.0% increase in cTnT risk for every 0.01 mg/L, adjusting for age and other factors. Increasing cTnT increased the all-cause mortality risk. The second CK-MB mass model revealed age, ischemic heart disease, Kt/V and creatinine as independent predictors but not CK-MB mass. The third model tested cTnT and CK-MB mass together and revealed age, ischemic heart disease, albumin and cTnT as independent predictors of death. Troponin T had high predictive power according to the narrow confidence interval.
Discussion Troponin T and CK-MB mass were significantly elevated in the group of underweight patients. Correlation analysis among cardiac markers and nutritional parameters showed a significant inverse association between troponin T and triceps skinfold, as well as between CK-MB mass and triceps skinfold. In men troponin T also correlated inversely with % body fat and body mass index. In addition, there were significant associations between myoglobin and mid-arm circumference, mid-arm muscle circumference, as well as albumin. Comparison of markers of myocardial injury mortality predictive power for all-cause deaths in hemodialysis patients revealed only one independent predictor – troponin T. CK-MB mass was selected as a potential predictor but not myoglobin. Among nutritional parameters albumin was selected as an independent predictor of mortality and anthropometric parameters also appeared as potential predictors of mortality (mid-arm circumference and mid-arm muscle circumference). Among other variables age, ischemic heart disease and left ventricular hypertrophy were independent predictors of mortality. It is well-known that in the general population obesity is associated with risk of developing cardiovascular disease and increased mortality.16 However, several studies have described an inverse correlation between BMI and mortality in patients with coronary artery disease.17–19 This is the socalled ‘‘obesity paradox’’. Could it be extended to the results of this study? Similarly, malnutrition is a very important problem in the hemodialysis population and is related to inflammation and atherosclerosis.20,21 Moreover, overweight patients survive for longer than others and are prominent in the dialysis population.7,8,22 This is known ‘‘reverse epidemiology’’ and it is similar to ‘‘obesity paradox’’. This is important because heart disease might then be prevented. Nowadays measurement of cardiac damage markers is established as a standard diagnostic procedure for acute coronary syndrome.2 In addition, these markers are elevated in stable patients and also have mortality prognostic value.3,4 However, only a few studies have evaluated the relation Table 5. Predictors of all-cause mortality in the cohort. Multivariate Cox proportional hazard model.
Table 4. Cox proportional hazard model univariate test of parameters, all-cause mortality in the cohort. Parameter
Hazard ratio (CI)
Age (years) Nephrosclerosis Diabetes mellitus IHD LVH IPI Kt/V Creatinine cTnT * 100 CK-MB mass Albumin MAC MAMC
1.06 3.37 2.45 3.50 1.66 1.88 0.18 0.99 1.04 1.18 0.88 0.93 0.89
(1.03–1.08) (1.58–7.18) (0.87–6.92) (2.05–5.96) (0.95–2.87) (1.32–2.69) (0.04–0.68) (0.99–0.99) (1.01–1.07) (1.05–1.33) (0.82–0.95) (0.87–0.99) (0.82–0.97)
p 50.01 50.01 0.09 50.01 0.07 50.01 50.05 50.01 50.01 50.01 50.01 50.05 0.01
Hazard ratio – Exp (B), CI – 95% confidence interval of Exp (B), p-significance of coeff.
Variable cTnT model Age (years) IHD LVH Albumin cTnT * 100 CK-MB mass model Age (years) IHD Kt/V Creatinine cTnT/CK-MB mass model Age (years) IHD Albumin cTnT * 100
Hazard ratio (CI)
p
1.05 2.21 1.77 0.92 1.04
(1.02–1.08) (1.24–3.94) (41.00–3.14) (0.85–0.99) (41.00–1.07)
50.01 50.01 50.05 50.05 50.05
1.04 1.91 0.14 0.99
(1.01–1.06) (1.06–3.45) (0.03–0.65) (0.99–0.99)
50.01 50.05 50.05 50.01
1.04 2.07 0.91 1.04
(1.02–1.07) (1.16–3.69) (0.84–0.98) (1.01–1.07)
50.01 50.05 50.05 50.01
Hazard ratio – Exp (B), CI – 95% confidence interval of Exp (B), p-significance of coeff.
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DOI: 10.3109/0886022X.2014.918814
between nutrition and standard myocardium damage markers in the normal population. As expected, in this population obese patients with non ST segment elevation myocardial infarction have higher levels of troponin T. This is related to metabolic factors rather than to obesity per se.9 Therefore the relation between nutrition and cardiac damage markers is important. A few studies investigated this relation by the way but none of them used anthropometric parameters except for body mass index.23,24 However, classic anthropometric parameters are more specific than body mass index or albumin in the evaluation of nutrition, as they estimate the percentage of body fat and body muscle. Inverse correlations of troponin T and anthropometric parameters have not been reported before. A relationship is expected because malnutrition, inflammation and atherosclerosis are associated and the validity of troponin T as a myocardial injury marker has been confirmed. However, testing the correlation of C- reactive protein in the 77 patients in this study did not reveal any significant correlation with anthropometric or cardiac marker parameters (results not presented). Thus, whether cardiac disease drives changes in anthropometry or whether another process is responsible for the changes in both cardiac disease and anthropometry (i.e., malnutrition, inflammation, time in dialysis, etc.) remain to be elucidated. In any case there is a need to prevent and cure malnutrition in these patients. On the other hand a direct correlation between myoglobin and parameters that estimate body muscle is expected, because myoglobin is not specific only for myocardium. Serum troponin T has been documented as a marker of ischemic heart disease and left ventricular hypertrophy, as well as a mortality predictor in normal and hemodialysis population in many studies.25 However, standard ischemic heart injury markers – myoglobin and creatinine kinase MB mass were not compared with troponin T in predicting outcome for these patients. We compared the value of troponin T, creatine kinase MB mass and myoglobin as mortality predictors in our hemodialysis patients. Although, all studied myocardial injury parameters are reliable in detecting acute coronary damage in the normal population, only troponin T predicted all-cause mortality in our patients. Therefore, troponin T is a very important parameter in assessing the risk of all-cause mortality for these patients and it was the optimal parameter among cardiac markers of myocardial injury in our study. Although, the pathophysiology of this elevation remains to be elucidated, association between elevated cTnT and significant CAD stenosis does not vary with renal function as validated by coronary angiography.26 Patients with elevated troponin T had lower Kt/V but Kt/V was not a significant independent parameter in the troponin model of survival analysis. This lower Kt/V could be a consequence of a slower blood pump during dialysis in patients with ischemic heart disease. The results of our study showed a gender difference and correlation between cardiac troponins and anthropometric parameters was much stronger in men. Moreover, anthropometric parameters were inversely related to mortality, again much more closely in men. This accords with our previous study where similar relations were found.8 The difference is not easy to explain. Although the Kt/V index was lower in men, it was still within normal limits. A difference in the
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onset of coronary heart disease is found in the general population where women develop coronary artery disease at an older age than men27 but in our study, more women than men had ischemic heart disease and they were of a similar age as the men. One could speculate that malnutrition in men could be related to this difference, but according to percentage of body fat, 15% of our patients were underweight, both men and women. In addition, there were no difference in cardiac markers between men and women. This study has a few drawbacks. Although the patients were monitored for many years and the findings are realistic, this is a small investigation so it should be viewed critically. In addition, new tests of troponin are more sensitive and less specific and will include cases without cardiac pathology. However, this will not erode our results because analysis of higher levels of troponin T (40.01 mg/L) will give the same relations. Repeated measures were not made due to financial resource limitations. Thus, our study raises more questions for further investigations than answers. Troponin T and CK-MB mass were significantly elevated in the underweight patient group. Troponin T was the only independent predictor of mortality of hemodialysis patients among myocardial injury markers.
Declaration of interest This work was supported by the Ministry of Science and Ecology of Serbia, contract 175089. None of the authors has any conflict of interest.
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11. Miskulin DC, Athienites NV, Yan G, et al. Hemodialysis (HEMO) Study Group. Comorbidity assessment using the Index of Coexistent Diseases in a multicenter clinical trial. Kidney Int. 2001;60:1498–1510. 12. National kidney foundation NKF K/DOQI guidelines 2000: Appendix VII: methods for performing anthropometry and calculating body measurements and reference tables. Available at: http://www.kidney.org/professionals/ kdoqi/guidelines_updates/ nut_appx07a.html. Accessed January 22, 2014. 13. Joint expert consultation. Chapter 8: health, obesity and energy values of dietary factors. In: Facts and oils in human nutrition. Food and Agriculture Organization of the United Nations. Available at: http://www.fao.org/docrep/v4700e/V4700E0d.htm. Accessed January 22, 2014. 14. BMI as an indicator of CED (Chapter 3). In: Body mass index. A measure of chronic energy deficiency in adults. Food and Agriculture Organization of the United Nations. Available at: http:// www.fao.org/docrep/t1970e/t1970e04.htm. Accessed January 22, 2014. 15. Daugirdas JT. Second generation logarithmic estimates of singlepool variable volume Kt/V: an analysis of error. J Am Soc Nephrol. 1993;4:1205–1213. 16. Poirier P, Giles TD, Bray GA, et al. American Heart Association; Obesity Committee of the Council on Nutrition, Physical Activity, and Metabolism. Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss: an update of the 1997 American Heart Association Scientific Statement on Obesity and Heart Disease from the Obesity Committee of the Council on Nutrition, Physical Activity, and Metabolism. Circulation. 2006;14:898–918. 17. Romero-Corral A, Montori VM, Somers VK, et al. Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies. Lancet. 2006;19:666–678.
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18. Oreopoulos A, Padwal R, Norris CM, Mullen JC, Pretorius V, Kalantar-Zadeh K. Effect of obesity on short- and long-term mortality postcoronary revascularization: a meta-analysis. Obesity (Silver Spring). 2008;16:442–450. 19. Horwich TB, Fonarow GC. Measures of obesity and outcomes after myocardial infarction. Circulation. 2008;118:469–471. 20. Pecoits-Filho R, Lindholm B, Stenvinkel P. The malnutrition, inflammation, and atherosclerosis (MIA) syndrome – the heart of the matter. Nephrol Dial Transplant. 2002;17:28–31. 21. Kalantar-Zadeh K, Ikizler TA, Block G, Avram MM, Kopple JD. Malnutrition-inflammation complex syndrome in dialysis patients: causesand consequences. Am J Kidney Dis. 2003;42: 864–881. 22. Zoccali C. The obesity epidemics in ESRD: from wasting to waist? Nephrol Dial Transplant. 2009;24:376–380. 23. Trimarchi H, Muryan A, Campolo-Girard V, et al. Elevated pro-brain natriuretic peptide, troponin T and malnutrition inflammatory score in chronic hemodialysis patients with overt cardiovascular disease. Nephron Clin Pract. 2011;117: 198–205. 24. Trimarchi H, Muryan A, Dicugno M, et al. In hemodialysis, adiponectin, and pro-brain natriuretic peptide levels may be subjected to variations in body mass index. Hemodial Int. 2011; 15:477–484. 25. Ferna´ndez-Reyes MJ, Mon C, Heras M, et al. Predictive value of troponin T levels for ischemic heart disease and mortality in patients on hemodialysis. J Nephrol. 2004;17: 721–727. 26. Heitner JF, Curtis JP, Haq SA, Corey GR, Newby LK, Jollis JG. The significance of elevated troponin T in patients with nondialysis-dependent renal insufficiency: a validation with coronary angiography. Clin Cardiol. 2005;28:333–336. 27. Maas AH, Appelman YE. Gender differences in coronary heart disease. Neth Heart J. 2010;18:598–603.
CLINICAL STUDY
A comparison of markers of myocardial injury and their relation to nutritional parameters in hemodialysis patients Milan D. Stosovic1, Sanja Dj. Stankovic2, Mirjana Lj. Stanojevic1, Sanja P. Simic-Ogrizovic1, Dijana B. Jovanovic1, and Radomir T. Naumovic1 Clinic of Nephrology, Clinical Center of Serbia, Belgrade, Serbia and 2Center for Medical Biochemistry, Clinical Center of Serbia, Belgrade, Serbia
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1
Abstract
Keywords
Background: Serum cardiac troponin T (cTnT) is a valuable marker of ischemic heart disease (IHD) and left ventricular hypertrophy, as well as a mortality predictor in hemodialysis populations. We compared the value of cTnT, creatinine kinase (CK)-MB mass and myoglobin as mortality predictors in our hemodialysis patients and evaluated their relation to nutritional status. Methods: A total of 118 hemodialysis patients were prospectively studied from January 2004 to April 2013. Clinical and laboratory evaluations during the 12-month baseline period included the history of IHD, signs of left ventricular hypertrophy (LVH), Kt/V and serum cardiac markers together with the percentage of body fat (%fat), mid-arm circumference (MAC), mid-arm muscle circumference (MAMC), triceps skinfold (TSF) and BMI. Results: Underweight patients had significantly higher cTnT values (Mann–Whitney, p50.05). Correlation analysis (Spearman) showed an inverse association between cTnT and TSF ( ¼ 0.22, p50.05), as well as between CK-MB mass and TSF ( ¼ 0.26, p50.01). In men cTnT also correlated inversely with %fat ( ¼ 0.27, p50.05) and BMI ( ¼ 0.33, p50.05). In addition, myoglobin was correlated significantly with MAC, MAMC and albumin. Among cardiac markers cTnT was the only independent variable predicting mortality (Multivariate Cox regression, HR ¼ 1.04 CI (1.01–1.07); p50.01; measurement units 0.01 mg/L). Conclusion: Troponin T and CK-MB mass were significantly elevated in the underweight patient group. Troponin T was the only independent cardiac marker predictor of all cause mortality in our hemodialysis patients.
Anthropometry, cardiac markers, hemodialysis, nutrition
Introduction The prevalence of ischemic heart disease in hemodialysis patients is 10–20 times higher than that in the general population, with 50% mortality due to cardiovascular disease.1 In non-uremic patients with suspected myocardial damage determination of myoglobin, creatinine kinase MB (CK-MB) and troponins has been shown to be reliable for early diagnosis of myocardial damage and risk stratifications, as well as in stable coronary artery disease patients.2–4 However, in hemodialysis patients these markers are elevated and diagnosis of myocardial injury based on them is less reliable.5 In addition, reasons for their elevation are complex and due rather to chronic cell injury than to decreased clearance.5,6 Although other cardiac markers, such as BNP (or NT-proBNP and proBNP), are also very powerful biomarkers, at least for left ventricular hypertrophy diagnostics and mortality prediction in hemodialysis patients, they are not used for myocardial ischemic injury diagnosis. Another problem related to cardiovascular disease in this Address correspondence to Milan Stosovic, Clinic of Nephrology, Clinical Center of Serbia, Pasterova 2, Belgrade 11000, Serbia. Tel/Fax: +38 111657944; E-mail: [email protected]
History Received 23 January 2014 Revised 5 March 2014 Accepted 24 April 2014 Published online 19 May 2014
population is reverse epidemiology, as obese patients survive for longer than others.7,8 As expected, obese patients with non ST segment elevation myocardial infarction in the non-kidney disease population have a raised level of troponin T.9 The question arises about the relation of the traditional risk factor, obesity, and standard markers of myocardial injury in hemodialysis patients. Therefore the present 9-year prospective study compared mortality prediction of markers of myocardial injury and evaluated their relation to nutritional status.
Patients and methods This prospective observational study included a cohort of 118 patients (58 men) on maintenance hemodialysis in the Dialysis Unit of the Clinical Center of Serbia. The design of the study was approved by the Ethics Committee of the Clinical Center. All patients were monitored until their death (56 patients), kidney transplantation (7 patients), departure from the center (11 patients) or the end of the study on April 30, 2013 (24 men and 20 women). Baseline measurements were made for 12 months from 1st January to 31st December 2004, when the patients entered the
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study and involved clinical, anthropometrical (February) and monthly laboratory examinations. After that the participants were monitored continually. Besides clinical monitoring and laboratory testing, anthropometric measurements were done every year until end of the study. The patients were dialyzed three times per week for 3–5 hours using bicarbonate solutions. The dialyzers were never reused. A standard diet was prescribed for hemodialysis patients10 that did not change during the observation period. Predialysis blood pressure was measured at the beginning of every dialysis session. Predialysis mean arterial pressure (MAP) was calculated from the formula [MAP ¼ diastolic + (systolic diastolic)/3]. Patients with a history or signs of ischemic heart disease (IHD) and left ventricular hypertrophy (echocardiography and electrocardiography, LVH) were considered to have this co-morbidity. Overall comorbidity was assessed using the index of physical impairment (IPI), which has three Physical Impairment categories: 0 – Normal function; 1 – Mild/moderate impairment; 2 – Serious/severe impairment.11 Fat percentage (%fat) was determined from the sum of triceps (TSF), biceps, suprailiac and subscapular skinfolds, as recommended by the DOQI Clinical Practice Guidelines.12 Normal intervals were 12–20% for men and 20–30% for women.13 The BMI normal interval was 18.6–24 kg/m2.14 Mid-arm muscle circumference (MAMC) was calculated from mid-arm circumference (MAC) from the formula [MAMC (cm) ¼ MAC (cm) 0.314 TSF (mm)]. Kt/V was obtained using the second generation Daugirdas formula.15 Kt=V ¼ lnðR 0:008 tÞ þ ð4 3:5RÞ UF=W where R ¼ post-dialysis/pre-dialysis blood urea nitrogen, t ¼ dialysis hours, UF ¼ pre-post dialysis weight change and W ¼ post-dialysis weight. Before the second dialysis/week blood samples were taken into Vacutainer tubes (BD Vacutainer Systems, Franklin Lakes, NJ). Blood samples for cardiac markers were collected during February 2004; one sample from each patient at the same time as the anthropometric measurements were made. They were not taken if there was evidence of stenocardia or an acute coronary event in the preceding 2 weeks, so the cardiac markers levels represent chronic elevations. Serum troponin I (cTnI), the MB fraction of creatine kinase and myoglobin concentrations were measured using microparticle enzyme immunoassays on an AxSYM System (Abbott Diagnostics, Wiesbaden, Germany). Serum troponin T was measured by an electrochemiluminescence immunoassay on a Roche Elecsys 2010 automated analyzer (Roche Diagnostics, Mannheim, Germany). Serum urea, creatinine, calcium, phosphate and hemoglobin levels were determined using routine clinical assays. All the following assays were validated in our laboratory. For the Roche Troponin T assay the limit of detection was 0.01 mg/L, with intra- and inter-assay coefficients of variation (CV) between 1.1% and 2.3%. For the AxSYM Troponin-I ADV assay 0.02 mg/L was the limit of detection with intra- and inter-assay CV between 4.3% and 9.4%. For the AxSYM CK-MB assay the limit of detection was 0.7 mg/L, with intra- and inter-assay CV between 1.2% and 7.0%. For the AxSYM Myoglobin assay limit of detection
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was 51.0 mg/L, with intra- and inter-assay CV between 3.0% and 5.1%. While 94 patients had measurable levels of cTnT only 16 patients had measurable levels of cTnI and they were high (from 0.10 to 8.90 mg/L). Therefore cTnI was omitted from further analysis. Statistical methods Predictor variables used in the Cox regression were derived from data in the baseline period. Differences between the groups were estimated using the t-test or Mann–Whitney test. Chi square with Yates’s correction and Fisher exact test were used for frequencies. Pearson or Spearman correlation coefficients were employed to detect associations among predictor variables. The parameters were tested in the baseline period. The cut-off point for statistical significance was 0.05. In order to determine collinearity for survival analysis correlation testing revealed marked associations among the cardiac markers. cTnT and CK-MB mass are potentially collinear (Spearman, ¼ 0.58, p50.01 and myoglobin with CK-MB mass (Spearman, ¼ 0.28, p50.01). Correlations among the anthropometric parameters were strong. Body fat percentage was collinear (Pearson, r40.5, p50.01) with TSF, MAC and BMI. MAC was collinear with %fat, TSF, MAMC, BMI and BW. BMI was collinear with %fat, MAC, MAMC, TSF and BW. The effect of each factor on patient survival was examined by univariate survival analysis. The Cox proportional hazard model was employed using time-independent covariates. Proportional hazards were tested by fitting time-varying models. The outcome was death and all the other patients were censored (transplanted, left the center, alive at the study end on April 30, 2013). Survival time was measured in months (from the beginning of the study until death or censoring). All variables were tested as potential predictors of death in the univariate analysis, including demographic, primary kidney disease data, time in dialysis, laboratory and clinical data, as well as cardiac markers and anthropometric parameters, in order to adjust the analysis for these variables. Variables that were potential predictors of death in univariate analysis (p50.10) were tested in the multivariate Cox proportional hazard model using the time-independent covariates, forward stepwise method. In each model the number of variables was at least ten times less than the number of patients.
Results Demographic data and primary kidney disease for all patients at the time of their inclusion in the study are presented in Table 1. Glomerulonephritis was the most frequent disease, while hypertension and diabetes together were responsible for end stage renal disease in 15.2% of the patients. Laboratory, clinical data and cardiac markers are shown in Table 2. Nutritional variables determined in the same period are presented in Table 3 separately for men and women. Using FAO (Food and Agriculture Organization) normal intervals for %fat, the patients were divided into underweight, normal weight and overweight. Among our patients 15.5% men and
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Table 1. Demographic variables and diagnosis. Parameter
Value
Sex Men Women Age (years)1 Time in dialysis (months)2 Diagnosis3 Chronic glomerulonephritis Hypertension (nephrosclerosis) Polycystic kidney disease Chronic interstitial nephropathy4 Diabetes Systemic diseases Other Unknown
58 60 55.0 (13.3) 64 (35 to 126) 16.9 8.5 11.0 11.9 6.7 5.9 17.8 21.2
(20) (10) (13) (14) (8) (7) (21) (25)
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1 – Mean (SD); 2 – median (interquaritle value); 3 – percent (count); 4 – Chronic interstitial, nephropathy includes Balkan endemic nephropathy, Chronic interstitial nephritis and Chronic pyelonephritis.
Figure 1. Underweight patients had higher serum levels of troponin T and creatinine kinase MB mass.
Table 2. Laboratory, clinical data and cardiac markers. Parameter
Mean (SD)
Normal values
Kt/V1 1.36 MAP (mmHg)1 95 IHD3 33.9 3 LVH 30.5 IPI2 0 Hemoglobin (g/L)1 100.1 Urea (mmol/L) 25.8 Creatinine (mmol/L)1 895 Calcium (mmol/L)1 2.31 Phosphorus (mmol/L)1 1.61 cTnT (mg/L) 2 0.038 CK-MBmass (mg/L)2 2.10 Myoglobin (mg/L)2 176
(0.24) 41.2 (10) 5100 (40) – (36) – (0 to 1) 0* (15.2) M: 138–175 W: 119–157 (3.7) 2.5–7.5 (167) M: 53–124 W: 53–106 (0.15) 2.10–2.70 (0.35) 0.80–1.60 (0.012 to 0.104) 50.01 (1.35 to 3.10) 3.8 (139 to 263) 5116.3
1 – Mean (SD); 2 – median (interquaritle value); 3 – percent (count); *It is normal to have zero comorbidity, range (0–3); Normal values of laboratory analysis are for health persons not for hemodialysis patients; MAP – mean arterial pressure; IHD – ischemic heart disease; LVH – left ventricular hypertrophy; IPI – index of physical impairment; cTnT – troponin T; CK-MBmass – creatinine kinase MB mass; M – men; W – women.
Table 3. Nutritional variables. Parameter Body weight (kg) BMI (kg/cm2) Percentage of body fat Mid-arm circumference (cm) Triceps skinfold (mm) Mid-arm muscle circumference (cm) Albumin (g/L)
Men 71.1 23.4 21.0 28.4
(13.1) (3.7) (7.7) (4.4)
Women 57.0 22.1 28.6 26.6
Normal values
(10.2) – (3.5) 18.6–24.9 (7.4) M: 12–20; F: 20–30 (4.2) –
11.2 (5.9) 24.9 (3.0)
15.7 (7.3) 21.7 (2.5)
– –
41.3 (3.7)
40.8 (3.2)
34–55
All variables were expressed as mean (SD).
15.2% women were found to be underweight, while 55.1% men and 42.4% women were overweight. IPI score was 0 in 70 patients, 1 in 40 patients and 2 in 8 patients. Ischemic heart disease was found in 32.7% of the men and 35% of the women.
Testing differences among the variables when patients were grouped by sex showed that men had a significantly lower Kt/V index than women (t-test, p50.01), but there were no significant differences in age, time in dialysis and comorbidity. The group of patients with IHD had a significantly higher level of cTnT (Mann–Whitney, p50.01), CK-MB mass (Mann–Whitney, p50.01), but lower concentrations of myoglobin (Mann–Whitney, p50.01). There was no significant difference in time in dialysis between the IHD groups (Mann– Whitney). In these patients creatinine was markedly lower than in patients without IHD (t-test, p50.01) and they were older (t-test, p ¼ 0.01). Kt/V was significantly lower in the IHD group (mean value 1.27, t-test, p50.01). In addition IPI was higher (Chi-square, p50.01) and hypertension (Fisher, p50.05) and diabetes (Fisher, p50.01) were more frequent at primary diagnosis in IHD patients. The group of patients with LVH showed a significantly higher CK-MB mass (Mann–Whitney, p50.05), lower Kt/V (t-test, p50.01), higher MAP (t-test, p50.01) and lower concentrations of serum phosphate (t-test, p50.05). This group of subjects had a higher IPI score (Chi-square, p50.05) and diabetes more frequently at primary diagnosis (Fisher, p50.01). No significant difference in time in dialysis between the LVH groups (Mann–Whitney) was found. The group of underweight patients had significantly higher cTnT (Mann–Whitney, p50.05) and CK-MB mass (Mann– Whitney, p50.05) Figure 1. However, underweight patients were longer on dialysis (median 105 months) than normal and overweight subjects (median 58 months; Mann–Whitney, p50.05). No other variables were significant in this group of patients. In overweight patients cTnT levels were somewhat lower but the difference was not significant when compared with normal weight and underweight individuals. Correlation analysis between cardiac markers and nutritional parameters revealed an inverse association between cTnT and TSF (Spearman, ¼ 0.22, p50.05), as well as between CK-MB mass and TSF (Spearman, ¼ 0.26, p50.01). In men cTnT correlated inversely with %fat (Spearman, ¼ 0.27, p50.05), TSF (Spearman,
DOI: 10.3109/0886022X.2014.918814
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¼ 0.37, p50.01) and BMI (Spearman, ¼ 0.33, p50.05). In addition, myoglobin was significantly correlated with MAC (Spearman, ¼ 0.24, p50.01), MAMC (Spearman, ¼ 0.26, p ¼ 0.01) and albumin (Spearman, ¼ 0.23, p50.05). Correlations among cardiac markers and clinical and laboratory variables were also significant – cTnT and Kt/V (Spearman, ¼ 0.25, p50.01), CK-MB mass and Kt/V (Spearman, ¼ 0.25, p50.01), myoglobin and Kt/V (Spearman, ¼ 0.20, p50.05). In addition cTnT correlated significantly with phosphate (Spearman, ¼ 0.21, p50.05). Because our study included prevalent patients a correlation analysis (Spearman) was conducted for time in dialysis with all continuous variables. No significant correlation was found for cardiac markers. However, time in dialysis was associated with some anthropometric parameters (MAC ¼ 0.24, p50.01; MAMC ¼ 0.21, p50.05 and TSF ¼ 0.18,
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p50.05), as well as with albumin ( ¼ 0.22, p50.05), hemoglobin ( ¼ 0.33, p50.01), urea ( ¼ 0.18, p50.05) and creatinine ( ¼ 0.25, p50.01). Further exploration of the relation between cTnT and anthropometric parameters revealed that it was nonlinear but after transformation of cTnT (natural logarithm) it became linear. Figure 2 shows linear regression between % body fat and ln(cTnT), and TSF and ln(cTnT), where the relation between ln(cTnT) and anthropometric parameters is inverse and significant only for %fat and TSF. Unfortunately, during the 9-year period 56 patients (47.4%) died. Among them there were 26 men (46.4%) and 30 women (53.6%). ROC analysis for morality prediction was carried out for cardiac markers (Figure 3). Among them, area under curve (AUC) was largest for cTnT (AUC 0.65; 95% CI 0.55 to 0.75; p50.01) and also significant for CK-MB mass (AUC 0.64; 95% CI 0.54 to 0.74; p50.01). Among the anthropometric
Figure 2. Linear regression analysis for troponin T and anthropometric parameters (percentage of body fat and triceps skinfold).
Figure 3. Receiver operating characteristic analysis for morality prediction (for cardiac markers and anthropometric parameters).
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parameters (Figure 3), MAMC and MAC had similar values (AUC 0.36; 95% CI 0.26 to 0.46; p50.01). Other anthropometric parameters did not have a significant AUC. The curves of TSF and BMI in the ROC graph were between the MAMC curve and the reference line. Potential predictors of all-cause death were selected using the univariate Cox proportional hazard model (p50.10; Table 4). Among cardiac markers cTnT and CKMB mass were potential predictors of all cause mortality in these patients. The variable cTnT was multiplied by 100 for clearer presentation of the results. Potential predictors of mortality among the nutritional parameters were MAC, MAMC and albumin. Although this was the prevalent patient group, time in dialysis was not selected as a potential predictor of death. Among clinical and laboratory variables age, nephrosclerosis, diabetes mellitus, IHD, LVH, IPI, Kt/V and creatinine were selected by univariate analysis. However, diabetes mellitus was not proportional. As a rule of thumb, according to the number of patients only eleven variables were selected for multivariate analysis: age, nephrosclerosis, IHD, LVH, IPI, Kt/V, creatinine, albumin, MAMC, cTnT100 and CK-MB mass. MAC was collinear with MAMC. Since cTnT100 and CK-MB mass were collinear, three models were tested: cTnT100, CK-MB mass model and a model with both variables. The individual predictive power of each variable was evaluated using multivariate Cox regression. The results are presented in Table 5. In the cTnT model, age, ischemic heart disease, left ventricular hypertrophy and albumin were independent variables together with cTnT. Proportional hazard analysis indicated a 4.0% increase in cTnT risk for every 0.01 mg/L, adjusting for age and other factors. Increasing cTnT increased the all-cause mortality risk. The second CK-MB mass model revealed age, ischemic heart disease, Kt/V and creatinine as independent predictors but not CK-MB mass. The third model tested cTnT and CK-MB mass together and revealed age, ischemic heart disease, albumin and cTnT as independent predictors of death. Troponin T had high predictive power according to the narrow confidence interval.
Discussion Troponin T and CK-MB mass were significantly elevated in the group of underweight patients. Correlation analysis among cardiac markers and nutritional parameters showed a significant inverse association between troponin T and triceps skinfold, as well as between CK-MB mass and triceps skinfold. In men troponin T also correlated inversely with % body fat and body mass index. In addition, there were significant associations between myoglobin and mid-arm circumference, mid-arm muscle circumference, as well as albumin. Comparison of markers of myocardial injury mortality predictive power for all-cause deaths in hemodialysis patients revealed only one independent predictor – troponin T. CK-MB mass was selected as a potential predictor but not myoglobin. Among nutritional parameters albumin was selected as an independent predictor of mortality and anthropometric parameters also appeared as potential predictors of mortality (mid-arm circumference and mid-arm muscle circumference). Among other variables age, ischemic heart disease and left ventricular hypertrophy were independent predictors of mortality. It is well-known that in the general population obesity is associated with risk of developing cardiovascular disease and increased mortality.16 However, several studies have described an inverse correlation between BMI and mortality in patients with coronary artery disease.17–19 This is the socalled ‘‘obesity paradox’’. Could it be extended to the results of this study? Similarly, malnutrition is a very important problem in the hemodialysis population and is related to inflammation and atherosclerosis.20,21 Moreover, overweight patients survive for longer than others and are prominent in the dialysis population.7,8,22 This is known ‘‘reverse epidemiology’’ and it is similar to ‘‘obesity paradox’’. This is important because heart disease might then be prevented. Nowadays measurement of cardiac damage markers is established as a standard diagnostic procedure for acute coronary syndrome.2 In addition, these markers are elevated in stable patients and also have mortality prognostic value.3,4 However, only a few studies have evaluated the relation Table 5. Predictors of all-cause mortality in the cohort. Multivariate Cox proportional hazard model.
Table 4. Cox proportional hazard model univariate test of parameters, all-cause mortality in the cohort. Parameter
Hazard ratio (CI)
Age (years) Nephrosclerosis Diabetes mellitus IHD LVH IPI Kt/V Creatinine cTnT * 100 CK-MB mass Albumin MAC MAMC
1.06 3.37 2.45 3.50 1.66 1.88 0.18 0.99 1.04 1.18 0.88 0.93 0.89
(1.03–1.08) (1.58–7.18) (0.87–6.92) (2.05–5.96) (0.95–2.87) (1.32–2.69) (0.04–0.68) (0.99–0.99) (1.01–1.07) (1.05–1.33) (0.82–0.95) (0.87–0.99) (0.82–0.97)
p 50.01 50.01 0.09 50.01 0.07 50.01 50.05 50.01 50.01 50.01 50.01 50.05 0.01
Hazard ratio – Exp (B), CI – 95% confidence interval of Exp (B), p-significance of coeff.
Variable cTnT model Age (years) IHD LVH Albumin cTnT * 100 CK-MB mass model Age (years) IHD Kt/V Creatinine cTnT/CK-MB mass model Age (years) IHD Albumin cTnT * 100
Hazard ratio (CI)
p
1.05 2.21 1.77 0.92 1.04
(1.02–1.08) (1.24–3.94) (41.00–3.14) (0.85–0.99) (41.00–1.07)
50.01 50.01 50.05 50.05 50.05
1.04 1.91 0.14 0.99
(1.01–1.06) (1.06–3.45) (0.03–0.65) (0.99–0.99)
50.01 50.05 50.05 50.01
1.04 2.07 0.91 1.04
(1.02–1.07) (1.16–3.69) (0.84–0.98) (1.01–1.07)
50.01 50.05 50.05 50.01
Hazard ratio – Exp (B), CI – 95% confidence interval of Exp (B), p-significance of coeff.
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between nutrition and standard myocardium damage markers in the normal population. As expected, in this population obese patients with non ST segment elevation myocardial infarction have higher levels of troponin T. This is related to metabolic factors rather than to obesity per se.9 Therefore the relation between nutrition and cardiac damage markers is important. A few studies investigated this relation by the way but none of them used anthropometric parameters except for body mass index.23,24 However, classic anthropometric parameters are more specific than body mass index or albumin in the evaluation of nutrition, as they estimate the percentage of body fat and body muscle. Inverse correlations of troponin T and anthropometric parameters have not been reported before. A relationship is expected because malnutrition, inflammation and atherosclerosis are associated and the validity of troponin T as a myocardial injury marker has been confirmed. However, testing the correlation of C- reactive protein in the 77 patients in this study did not reveal any significant correlation with anthropometric or cardiac marker parameters (results not presented). Thus, whether cardiac disease drives changes in anthropometry or whether another process is responsible for the changes in both cardiac disease and anthropometry (i.e., malnutrition, inflammation, time in dialysis, etc.) remain to be elucidated. In any case there is a need to prevent and cure malnutrition in these patients. On the other hand a direct correlation between myoglobin and parameters that estimate body muscle is expected, because myoglobin is not specific only for myocardium. Serum troponin T has been documented as a marker of ischemic heart disease and left ventricular hypertrophy, as well as a mortality predictor in normal and hemodialysis population in many studies.25 However, standard ischemic heart injury markers – myoglobin and creatinine kinase MB mass were not compared with troponin T in predicting outcome for these patients. We compared the value of troponin T, creatine kinase MB mass and myoglobin as mortality predictors in our hemodialysis patients. Although, all studied myocardial injury parameters are reliable in detecting acute coronary damage in the normal population, only troponin T predicted all-cause mortality in our patients. Therefore, troponin T is a very important parameter in assessing the risk of all-cause mortality for these patients and it was the optimal parameter among cardiac markers of myocardial injury in our study. Although, the pathophysiology of this elevation remains to be elucidated, association between elevated cTnT and significant CAD stenosis does not vary with renal function as validated by coronary angiography.26 Patients with elevated troponin T had lower Kt/V but Kt/V was not a significant independent parameter in the troponin model of survival analysis. This lower Kt/V could be a consequence of a slower blood pump during dialysis in patients with ischemic heart disease. The results of our study showed a gender difference and correlation between cardiac troponins and anthropometric parameters was much stronger in men. Moreover, anthropometric parameters were inversely related to mortality, again much more closely in men. This accords with our previous study where similar relations were found.8 The difference is not easy to explain. Although the Kt/V index was lower in men, it was still within normal limits. A difference in the
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onset of coronary heart disease is found in the general population where women develop coronary artery disease at an older age than men27 but in our study, more women than men had ischemic heart disease and they were of a similar age as the men. One could speculate that malnutrition in men could be related to this difference, but according to percentage of body fat, 15% of our patients were underweight, both men and women. In addition, there were no difference in cardiac markers between men and women. This study has a few drawbacks. Although the patients were monitored for many years and the findings are realistic, this is a small investigation so it should be viewed critically. In addition, new tests of troponin are more sensitive and less specific and will include cases without cardiac pathology. However, this will not erode our results because analysis of higher levels of troponin T (40.01 mg/L) will give the same relations. Repeated measures were not made due to financial resource limitations. Thus, our study raises more questions for further investigations than answers. Troponin T and CK-MB mass were significantly elevated in the underweight patient group. Troponin T was the only independent predictor of mortality of hemodialysis patients among myocardial injury markers.
Declaration of interest This work was supported by the Ministry of Science and Ecology of Serbia, contract 175089. None of the authors has any conflict of interest.
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