The role of cardiorenal biomarkers for risk

http://informahealthcare.com/bmk ISSN: 1354-750X (print), 1366-5804 (electronic) Biomarkers, Early Online: 1–7 ! 2013 Informa UK Ltd. DOI: 10.3109/1354750X.2013.821522

ORIGINAL ARTICLE

The role of cardiorenal biomarkers for risk stratification in the early follow-up after hospitalisation for acute heart failure J. Tolonen1, J. P. E. Lassus2, K. Siirila-Waris1, T. Tarvasma¨ki1, K. Pulkki3, R. Sund4, K. Peuhkurinen5, M. S. Nieminen2, V.-P. Harjola1, and for the FINN-AKVA Study Group* Department of Medicine and 2Heart and Lung Center, Helsinki University Central Hospital, Helsinki, Finland, 3Islab, Kuopio, Finland, 4National Institute for Health and Welfare, Helsinki, Finland, 5Oulu Deaconess Medical Centre, Oulu, Finland

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Abstract

Keywords

Context: Cardiorenal biomarkers (CBs) predict outcome in acute heart failure (AHF). Objective: To evaluate CBs in early follow-up prognostication. Methods: In 124 AHF patients, levels of CystatinC, NT-proBNP and TroponinI measured five weeks from admission (W5) and relative change from day 2 (D2) were assessed for 6-month prognosis (mortality/HF hospitalization). Results: The combined end-point occurred in 33 patients (27%). D2-, W5-cystatin median, and lack of 30%decrease in NT-proBNP were independent predictors of outcome. Additionally, a risk score established from W5 CBs identified patients with very high event rate. Conclusions: CBs at early follow-up of AHF may guide risk stratification.

Acute heart failure, NT-proBNP, risk stratification, troponin

Introduction Short- and long-term survival of patients with acute heart failure (AHF) is dismal. In-hospital mortality is high (7–9%) (Cleland et al., 2000; Nieminen et al., 2006; Rudiger et al., 2005; Siirila¨-Waris et al., 2006). Twelve months after admission to hospital more than 25% of patients have died (Siirila¨-Waris et al., 2006). The readmission rate is high. In FINN-AKVA study six-month all-cause hospitalisation rate was 57% (Harjola et al., 2010). The aetiology of AHF varies and the clinical presentation is heterogeneous. Patients are elderly, have several co-morbidities, and the detection of individuals at risk of poor outcome is difficult exclusively on the basis of clinical findings.

*FINN-AKVA study group: V.-P. Harjola, K. Siirila¨ -Waris, M.S. Nieminen, Helsinki University Central Hospital; J. Melin, Central Finland Central Hospital; K. Peuhkurinen, Kuopio University Hospital; M. Halkosaari, Keski-Pohjanmaa Central Hospital; K. Ha¨nninen, Kymenlaakso Central Hospital; T. Ilva, T. Talvensaari, Kanta-Ha¨me Central Hospital; H. Kervinen, Hyvinkaä¨ Hospital; K. Kiilavuori, Jorvi Hospital; K. Majamaa-Voltti, Oulu University Hospital; H. Ma¨kynen, V. Virtanen, Tampere University Hospital; T. Salmela-Mattila, Rauma Hospital; K. Soininen, Kuusankoski Hospital; M. Strandberg, H. Ukkonen, Turku University Hospital; I. Vehmanen, Turku Hospital; E.-P. Sandell, Orion Pharma, Espoo, Finland. Study nurses: K. Hautakoski, Keski-Pohjanmaa Central Hospital; J. Lamminen, Hyvinkaä¨ Hospital; M.-L. Niskanen, Kuopio University Hospital; M. Pietila¨, Helsinki University Central Hospital; O. Surakka, Central Finland Central Hospital. Address for correspondence: Jukka Tolonen, Department of Medicine, Helsinki University Central Hospital, Box 340, FI-00029 HUS, Finland. Tel: +358-9-47171485. Fax +358-9-47174007. E-mail: [email protected]

History Received 22 April 2013 Revised 26 June 2013 Accepted 28 June 2013 Published online 23 July 2013

The prognostic role of cardiorenal biomarkers cystatin C, NT-proBNP and cardiac troponins in AHF patients has mainly been observed during hospitalisation (Arenja et al., 2012; Lassus et al., 2007; Lee et al., 2012; Malek et al., 2012; Manzano-Fernandez et al., 2011; Metra et al., 2012; O’Connor et al., 2011; Yu & Sanderson, 1999). Predischarge values of NT-proBNP have been shown to be best predictors of worse outcome (Bettencourt et al., 2004a, b; Logeart et al., 2004; Metra et al., 2007; Pimenta et al., 2007). Serial NT-proBNP monitoring in chronic HF has been shown to predict cardiovascular events (Bayes-Genis et al., 2007; Moertl et al., 2008). Yet, results from the natriuretic peptide-guided therapy in chronic HF have been inconsistent (Jourdain et al., 2007; Latini et al., 2006; Pfisterer et al., 2009). It seems reasonable to believe that patients with the worst prognosis have less improvement or even worsening of cardiorenal biomarkers early after in-hospital management compared with those having better outcome. Thus our aim was to evaluate whether cardiorenal biomarkers creatinine, cystatin C, cardiac troponin I (cTnI) and NT-proBNP measured early after hospital discharge could be used for detection of patients at the highest risk of adverse outcome. Consequently, the value in risk stratification of early follow-up levels of cardiorenal biomarkers 5 weeks (W5) from admission as well as the prognostic role of their change from initial day 2 (D2) to W5 were analysed.

Methods FINN-AKVA – Finnish Acute Heart Failure Study is a national multicentre study, which recruited all consecutive patients hospitalised for AHF between February and May


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2004 in 14 hospitals. In total 620 patients were included. This substudy was restricted to seven centers. 149 patients were recruited to the study. Patients not yet discharged from the index hospitalization, died or were re-hospitalised before the W5 time-point from admission were excluded (n ¼ 21). In addition, four patients did not have the complete series of cardiorenal biomarkers. Thus 124 patients were included in the final analysis. A flowchart of the study population is shown in Figure 1. Serial blood samples were obtained at D2 and W5. Absolute levels of NT-proBNP, cardiac troponin I (cTnI), creatinine and cystatin C and the change from D2 to W5 were analysed. Cystatin C (Dako, Glostrup, Denmark), NT-proBNP (Roche Diagnostics, Basel, Switzerland) and cTnI (Abbott Diagnostics, Abbott Park, IL), as well as, plasma creatinine (Roche Diagnostics, Basel, Switzerland) were analysed in a central laboratory. cTnI positivity was defined as 0.035 mg/l. For hemoglobin and sodium levels, admission samples (analysed locally with standard methods) were used to avoid bias by any corrective measures (i.e. blood transfusion) during hospitalisation. Estimated glomerular filtration rate (eGFR) was assessed by the 4-variable MDRD equation. Patients were followed 6 months from the W5 time-point, and mortality data were obtained from National Population Registry and data on hospitalisations from the National Hospital Discharge Registry. The main endpoint was a combination of 6-month HF hospitalisation and all-cause mortality. All patients gave written informed consent. FINN-AKVA study was approved by the national ethics committee and conducted in accordance with the declaration of Helsinki. Statistical analysis Several variables were compared between patients with/ without end point. Continuous variables were presented as

Biomarkers, Early Online: 1–7

means and standard deviations (SD) or as medians and interquartile ranges (IQRs) and compared by Student t-test (former) or by Kruskal–Wallis test (latter), the equality of variances (former) was tested by Levene’s test. Categorical variables were represented as counts and percentages (%) and compared by Mann–Whitney U-test. The point of statistical significance was set at p50.05. Cardiorenal biomarkers investigated were eGFR560 ml/ min/1.73 m2, median of plasma creatinine, cystatin C, and NT-proBNP as well as cTnI positivity at D2 and W5, In addition, a decrease in NT-proBNP at least 30% from D2 to W5 (due to known normal fluctuation between samples taken in different time from one individual we selected change which cannot be explained by normal variation), as well as, at least 25% increase in cystatin C and creatinine from D2 to W5 and D2 and W5 NT-proBNP ‘‘high’’ (Q4 in IQR [interquartile range]) were assessed. Log rank analysis was done for cardiorenal biomarkers and for evaluated risk classes. The results are presented as Kaplan Maier curves. In addition, area under curve (AUC) was calculated in Roc curve analysis. Hazard ratios were derived from the Cox proportional hazard model to identify predictors of 6-month combined end-point. The following biochemical markers were separately adjusted for age, gender, previous heart failure and systolic blood pressure at admission in Cox multivariable regression model; D2 and W5 eGFR560 ml/min/1.73 m2, cystatin C median, cTnI positivity, ‘‘high’’ NT-proBNP (Q4) and lack of 30% decrease in NT-proBNP from D2 toW5. The multivariable model for cTnI and NT-proBNP also included eGFR560 ml/min/1.73 m2 (D2 or W5) to adjust for renal function. We included also risk score based on observed W5 cardiorenal biomarkers. The patient obtained risk points (1 point from each parameter) if W5 NT-proBNP was ‘‘high’’ (Q4 in IQR), if W5 cystatin was median, if NT-proBNP did not decrease 30% from D2 to W5 or if in W5 was observed cTnI release (0.035 mg/l). Risk scores 0–4 were obtained from summarised points. Patients were divided in low (risk scores 0–1), intermediate (risk score 2) and high (risk scores 3–4) risk groups. Analyses were done using the SPSS statistical softwareÕ (IBM, Armonk, NY), version 19.0.

Results

Figure 1. A flow chart presentation of the study design. Reasons for exclusion: 21 patients were not discharged from index hospital, deceased or re-hospitalised before W5; 4 patients omitted total laboratory series. D2 ¼ day 2, W5 ¼ week five from admission.

Table 1 shows the characteristics of the study population and levels of the measured biomarkers. The combined end-point was observed in 27% (33/124) of the study cohort. 11 (9%) patients died and 27 (22%) were re-hospitalised for HF during follow-up. The number of patients with cTnI positivity decreased markedly from D2 (67 [54%]) to W5 (35 [28%]). Median NT-proBNP at D2 was 3248.5 (interquartile range [IQR] 1758-6326) pg/ml, and 2130 (959–4572) pg/ml at W5 in the study cohort. The median values for cystatin C were 1.21 (0.98–1.60) mg/l at D2 and 1.33 (1.02–1.68) mg/l at W5. Worsening renal function from D2 to W5, defined as increase in cystatin C 25% (n ¼ 22) or increase in creatinine 25% (n ¼ 23) were observed in approximately one-fifth of patients. A 30% decrease in NT-proBNP from D2 to W5 was seen in 67 (54%) patients.

The role of cardiorenal biomarkers for risk stratification

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Table 1. Characteristics of the study population and biomarker levels. Group comparison of patients with versus without combined-endpoint (all-cause mortality and heart failure hospitalisation).


All n ¼ 124 Age, years; mean (SD) Men; N ¼ (%) History of; N ¼ (%) Previous heart failure Coronary artery disease Myocardial infarction Cerebrovascular disease Chronic atrial fibrillation Hypertension Diabetes ACS on admission Systolic blood pressure (mmHg), mean (SD) Hyponatremia (n ¼ 122) Anemia (n ¼ 122) Troponin I positive (0.035 mg/l) D2 troponin I W5 troponin I Cystatin C (mg/l); median (IQR) D2 cystatin W5 cystatin Creatinine (mmol/l); median (IQR) D2 creatinine W5 creatinine NT-proBNP D2 NT-proBNP(pg/ml) median (IQR) W5 NT-proBNP(pg/ml) median (IQR) D2 NT-proBNP Q4 (6326 pg/ml) W5 NT-proBNP Q4 (4972 pg/ml) NT-proBNP decrease 30%

Patients with combined end-point n ¼ 33

71.7 (10.2) 73 (59)

70.4 (10.8) 23 (70)

66 (53) 59 (48) 29 (23) 20 (16) 31(25) 85 (69) 48 (39) 35 (28) 149 (33)

22 17 9 5 13 25 14 8 142

(67) (52) (27) (15) (39) (76) (42) (24) (33)

Patients without combined end-point n ¼ 91 72.2 (9.9) 50 (55)

p Value for difference p ¼ 0.4 p ¼ 0.14

44 42 15 18

(48) (46) (16) (20)

60 34 27 152

(60) (37) (30) (33)

p ¼ 0.07 p ¼ 0.6 p ¼ 0.5 p ¼ 0.9 p ¼ 0.026 p ¼ 0.3 p ¼ 0.6 p ¼ 0.6 p ¼ 0.12

16/122 (13) 39/122 (32)

6/32 (19) 16/32 (50)

10/90 (11) 23/90 (26)

p ¼ 0.3 p ¼ 0.011

67 (54) 35 (28)

21 (64) 14 (42)

46 (51) 21 (23)

p ¼ 0.17 p ¼ 0.035

1.21 (0.98–1.60) 1.33 (1.02–1.69) 87 (68–110) 89 (73–127) 3249 2130 31 31 67

(1758–6326) (959–4972) (25) (25) (54)

1.49 (1.06–2.23) 1.64 (1.12–2.13) 102 (81–125) 92 (82–149) 3410 2625 8 14 11

(1967–7093) (1568–7068) (24) (42) (33)

1.16 (0.92–1.42) 1.25 (0.98–1.48)

p ¼ 0.004 p ¼ 0.012

82 (67–100) 87 (71–112)

p ¼ 0.004 p ¼ 0.041

2901 1942 23 17 56

(1676–6414) (883–3899) (25) (19) (62)

p ¼ 0.361 p ¼ 0.018 p ¼ 0.9 p ¼ 0.007 p ¼ 0.006

Numbers are counts and percentages (%) for categorical variables, mean and standard deviation (SD) or median and interquartile range (IQR) for continuous variables; ACS, acute coronary syndrome; LVEF, left ventricular ejection fraction; hyponatremia (plasma sodium5135 mmol/L); anemia, WHO definition of haemoglobin 5130 g/L for men and 5120 g/L for women; D2, day 2; W5, week 5; eGFR, estimated glomerular filtration rate; Q4, highest quartile in IQR.

Patients with a subsequent endpoint had worse renal function both at D2 and W5 and higher NT-proBNP at W5. In log rank analysis patients with NT-proBNP decrease 30% had better outcome during follow-up (Figure 2A). Furthermore, W5 cystatin median was associated with an increased risk of the combined end point (Figure 2B). In addition, patients with W5 NT-proBNP in the highest quartile (Q4; 4972 pg/ml) had worse prognosis compared to all the other quartiles (Figure 3). If all-cause mortality alone was selected as a secondary outcome W5 cystatin median and W5 NT-proBNP Q4 remained prognostic predictors for poorer outcome (p50.05 for both). The other markers did not reach statistically significance but the number of events was very low (n ¼ 11). Univariate HRs for the CB in prediction of prognosis are shown in Table 3. Positive W5 cTnI, cystatin C above median (D2 and W5) and NT-proBNP Q4 at W5 as well as lack of 30% decrease in NT-proBNP from D2 to W5 were each associated with worse outcome. In Cox multivariate analysis adjusted with age, gender, previous heart failure and systolic blood pressure at admission, D2 cystatin median (HR 2.47, CI 1.07–5.70), W5 cystatin median (HR 2.42, CI 1.13– 5.18), and lack of 30% reduction in NT-proBNP (HR 2.25, CI 1.06–4.76) remained independent predictors for the combined end-point (Table 2).

Results from estimated risk scores are presented in Table 3. The risk score classified patients in categories with low (32% of patients), intermediate (49% of patients) and high (19% of patients) risk. The observed rates of adverse events during follow-up were 15%, 23% and 57%, respectively (Figure 4). In ROC curve analysis, the risk score had an AUC of 0.70 (CI 0.60–0.81) for prediction of the combined end-point, which was higher than any of the individual cardiorenal biomarkers.

Discussion With an AUC of only 0.7 after combining multiple prognostic markers, even if statistically significant, one may argue that cardiac biomarkers are not of great importance for prognostication but that they might be of some importance or ‘‘that the data supports the association of cardiorenal biomarkers with prognosis in patients following hospitalization for CHF’’. Our study suggests that measuring cardiorenal biomarkers in the early follow-up after AHF hospitalisation may be helpful for prediction of the subsequent risk of mortality and HF hospitalisation. The data support the association of cardiorenal biomarkers with prognosis in patients following hospitalization for AHF. Absolute levels as well as changes in cardiorenal biomarkers may be


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Figure 2. Log Rank analysis of combined end point survival rate presented as Kaplan Maier curves (W5 ¼ week 5). Risk scores are explained in Table 3. (A) Effect of 30% decrease in NT-proBNP (p ¼ 0.005). (B) Effect of W5 cystatin C median (1.33 mg/l) (p ¼ 0.008).

Figure 3. Effect of different levels of W5 NT-proBNP. Note that only group Q4 differs from other groups (p ¼ 0.029). Groups represent interquartile range for W5 NT-proBNP.


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Table 2. Hazard ratios for CBs as predictors of mortality and HF hospitalisation. Cox regression analysis Variable D2 cystatin C median W5 cystatin C median D2 NT-proBNP Q4 (6326 pg/ml) W5 NT-proBNP Q4 (4972 pg/ml) lack of 30% decrease in NT-proBNP D2 troponin I positivity (0.035 mg/l) W5 troponin I positivity (0.035 mg/l) D2 eGFR 5 60 ml/1.73m2/min W5 eGFR 5 60 ml/1.73m2/min

Univariate HR (95% CI) 2.28 2.46 1.09 2.74 2.70 1.59 2.08 2.02 1.89

(1.11–4.71) (1.17–5.16) (0.49–2.42 (1.37–5.47) (1.32–5.56) (0.78–3.22) (1.04–4.16) (1.02–4.01) (0.95–3.73

p

Multivariable HR (95% CI)

p

0.026 0.018 0.831 0.004 0.007 0.203 0.037 0.045 0.069

2.47 (1.07–5.70) 2.42 (1.13–5.18) – 2.05 (0.98–4.28)* 2.25 (1.06–4.76)* – 1.71 (0.83–3.53)* 2.15 (0.97–4.79) 2.03 (0.98–4.23)

0.034 0.023 – 0.055 0.034 – 0.149 0.061 0.058


Cox multivariate regression analysis for 6-month combined end point (mortality and heart failure hospitalisation). Adjustment was made for age, gender, previous heart failure and systolic blood pressure at admission. *Models with cTnI and NT-proBNP were adjusted also to D2 or W5 eGFR560 ml/min/1.73m2; Q4, highest quartile in IQR (interquartile range); eGFR, estimated glomerular filtration rate (by the MDRD equation).

Figure 4. Use of risk classes for risk evaluation. Low risk class accounts scores 0 and 1, intermediate class risk score 2 and high risk class scores 3 and 4. Especially high risk class predicted poorer outcome (p ¼ 0.001).

Table 3. Risk score based on cardiorenal biomarkers at week 5 (W5). Variable W5 NT-proBNP Q4 (4972 pg/ml) W5 cystatin median (1.33 mg/l) Lack of 30% decrease in NT-proBNP W5 troponin I positivity (0.035 mg/l) Maximum points

Points 1 1 1 1 4

useful in predicting prognosis and combining them into a simple risk score gives an easy tool for risk stratification in clinical practice. The present study adds to previous observations of the importance of cystatin C in prognostication during AHF hospitalisation (Lassus et al., 2007) with the implication of the utility of cystatin C for risk stratification also when measured during early follow-up. In addition, our results support previous finding of importance of initial renal function as prognostic marker (Anwaruddin et al., 2006; Lassus et al., 2007, 2010; Pimenta et al., 2007; Yamashita et al., 2010).


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The lack of decrease in NT-proBNP by at least 30% in the early follow-up was associated with increased risk of combined end-point. In patients hospitalised due to acute dyspnoea – not solely AHF – and with baseline NT-proBNP 300 pg/ml, the serial measurement of NT-proBNP 30 days after admission combined with serial evaluation of quality of life had value for evaluation of combined 1-year mortality and HF hospitalisation risk (Shah, 2010). Especially, if NT-proBNP decreased 25% within 30 days, the risk decreased significantly. The significance of NT-proBNP in the follow-up of AHF patients is largely in line also with a previous study which measured follow-up NT-proBNP at 3 months (Moertl, 2008). However, many patients die or are re-hospitalized before 3 months. Earlier re-assessment may be warranted and we show that values measured already at W5 follow-up and the change from in-hospital levels is of clinical importance. This should encourage the practice of early follow-up visits approximately 1 month after discharge and the use of cardiorenal biomarkers for risk stratification. NT-proBNP levels in patients hospitalised for AHF are known to decrease from admission to discharge, and both the in-hospital decrease and discharge levels have been shown to predict outcome (Baggish et al., 2010; Bettencourt et al., 2004b; Hamada et al., 2005; Logeart et al., 2004; Metra et al., 2007). Of interest, serum NT-proBNP has been shown to decrease very early, and according to one study, the lowest level during the hospital stay is reached already at 48 h (Metra et al., 2007). Analysis from Italian RED-study has shown a powerful negative predictive value for future cardiovascular outcomes if BNP was reduced 4 46% from admission to discharge in patients hospitalised due to AHF (Di Somma et al., 2010). In the present study we did not analyse the discharge values. However, since the present study shows that the levels of NT-proBNP further decrease early after discharge from hospital, it seems obvious that the values measured at follow-up should be used to re-evaluate the risk of adverse outcome supporting the finding of Shah et al. (2010). In fact, in our study slightly more than half of the patients had a decrease of at least 30% at week 5-follow-up. Patients with acute coronary syndromes were included in the present study, whereas they have often been excluded in previous studies (Baggish et al., 2010; Moertl et al., 2008). However, we feel that it is of importance to include population representing one third of all patients with AHF on admission. Of interest, neither ACS (data not shown) nor cardiac cTnI positivity at D2 had any prognostic significance in the present study. However, positive TnI at W5 as a part of the risk score identified patients with increased rates of the combined end point. cTnI positivity most likely reflected increased wall tension, severity of disease or other factors (e.g. anaemia) provoking subendocardial injury even in the absence of coronary artery obstruction (Januzzi et al., 2012). In a previous study, cardiac cTn positivity during hospital treatment did not have any independent prognostic value (Ilva et al., 2008), but there are also conflicting data (Arenja et al., 2012; Lee et al., 2012; Malek et al., 2012; Metra et al., 2012; O’Connor et al., 2011). High NT-proBNP (Q4) at W5 was also associated with poor 6-month outcome. However, sole use of a single biomarker does not usually improve clinical


risk prediction. Biomarkers should be added to available clinical risk scores or used together as multimarker strategies (Lassus et al., 2013). The present study suggests that together with cystatin C use of W5 NT-proBNP and cTnI, as well as the relative change of NT-proBNP, may be considered as part of risk score during early follow-up. Limitations of our study include low event rates during follow-up. The main explanation for this is that patients attending early follow-up had to survive the initial hospitalisation and the first month after discharge, a period with the highest risk of death or re-hospitalisation after AHF. As this study did not measure biomarkers at discharge, comparison of changes in biomarker levels between D2, discharge and W5 was not possible. However, as previously noted most of the change in NT-proBNP has been shown to occur during the first few days (Gegenhuber et al., 2006; Metra et al., 2007) because of the upfront intensity of acute care and resolution of AHF symptoms. Thus we feel that 48 h is a relevant time point for assessing the response to early therapy. Secondly, the assessment of cTnI was performed with a standard assay, with lower sensitivity than the newer high-sensitivity cTn assays. Finally, the risk score developed in this study of rather limited size needs to be validated in larger cohorts. Nevertheless, risk scores based on biomarkers may be helpful in risk stratification especially for clinicians less familiar with the challenging clinical evaluation of HF patients. In conclusion, cardiorenal biomarkers measured during early follow-up may be useful for risk stratification, and allow dynamic re-evaluation of prognosis. We propose the use of a risk score based on W5 cardiorenal biomarkers. Importantly, efforts should be made to translate the signs of elevated risk to individualized management of HF patients in order to improve their prognosis.

Acknowledgements Study nurses Tina Svahn and Mervi Pietila¨ are deeply acknowledged. Declaration of interest The study was supported by Helsinki District University Hospital, Finnish Foundation for Cardiovascular Research, Paulo Foundation and an unrestricted grant from Roche Diagnostics and Abbott Laboratories. Authors V-P.H., J.L. and K.P. are members of advisory board for Roche Diagnostics in Finland.

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