Identification of novel biomarkers for predicting outcome of acute and chronic kidney disease

272 Pages·2016·60.26 MB·English

Identification of novel biomarkers for predicting outcome
of acute and chronic kidney disease
DISSERTATION ZUR ERLANGUNG DES DOKTORGRADES
DER NATURWISSENSCHAFTEN (DR. RER. NAT.)
DER FAKULTÄT FÜR BIOLOGIE UND VORKLINISCHE MEDIZIN
DER UNIVERSITÄT REGENSBURG
vorgelegt von
Helena U. Zacharias
aus
Regensburg
im Jahr 2016
Das Promotionsgesuch wurde eingereicht am:
Die Arbeit wurde angeleitet von:
Prof. Dr. Wolfram Gronwald
Unterschrift:
Helena Zacharias
2
This Ph.D. thesis has in parts already been published in [Zacharias 2012,Zacharias et al.
2013a),Zacharias et al. 2013b),Zacharias et al. 2015,Hochrein et al. 2015].
Fig. 4.2, page 39, Fig. 4.3, page 40, Fig. 4.4, page 41, and Fig. 4.6, page 47, have al-
ready been published in [Zacharias 2012] and were provided by me.
A section on page 45 has already been published in a slightly altered version in [Zacharias et
al. 2013b)]. Claudia Samol wrote the first draft of the manuscript and the published article
had been revised by Prof. Dr. Wolfram Gronwald and Prof. Dr. Peter Oefner.
A section on page 49-50 has already been published in a slightly altered version in [Zacharias
et al. 2013b)].
Sections on pages 52-53 and 56-58 have already been published in slightly altered versions
in [Zacharias et al. 2013b)]. Dipl. Math. Jochen Hochrein wrote the first draft of the
manuscript and the published article had been revised by Prof. Dr. Wolfram Gronwald and
Prof. Dr. Peter Oefner.
Section 4.3.2, page 61, has already been published in slightly altered versions in [Zacharias et
al. 2013b)]. I wrote the first draft of the manuscript and the published article had been revised
by Prof. Dr. Wolfram Gronwald and Prof. Dr. Peter Oefner.
Section 5.1.2.1, pages 67-68, has already been published in [Zacharias et al. 2013a)] and
[Zacharias et al. 2015] in a slightly altered version. I wrote the first drafts of the manuscripts
and the published articles had been revised by Prof. Dr. Wolfram Gronwald and Prof. Dr.
Peter Oefner. Patient handling as well as biofluid collection and assessment of clinical data had
been conducted at the University of Erlangen-Nuremberg.
Section 5.1.2.5, page 70-71, has already been published in [Hochrein et al. 2015] in a slightly
altered version, and is also part of Dipl. Math. Jochen Hochrein’s Ph.D. thesis [Hochrein
2016]. The reported analyses have been performed by Prof. Dr. Wolfram Gronwald and Clau-
dia Samol and the manuscript has been written by Prof. Dr. Wolfram Gronwald.
Sections on pages 71-72 have already been published in a slightly modified version in [Zacharias
etal. 2015]. Theclassification/prognosticationconceptofnestedcross-validationemployedhere
was implemented by Dipl. Math. Jochen Hochrein and is also part of his Ph.D. thesis [Hochrein
2016]. I employed this nested cross-validation and analyzed the corresponding results.
Section 5.1.2.8, page 73, has already been published in [Zacharias et al. 2015] in a slightly
modified version. I slightly modified the applied linear SVM cross-validation with optimization
of the cost parameter C from a previous R-code of Dipl. Math. Jochen Hochrein.
Section 5.1.2.9, page 73, and section 5.1.3.7, pages 85-86, have already been published in
[Zacharias et al. 2015] in slightly altered versions, and the presented concept as well as the
corresponding algorithm was developed by Dipl. Math. Jochen Hochrein. It is also part of his
Ph.D. thesis [Hochrein 2016]. I performed data analyses and wrote the first draft of [Zacharias
et al. 2015] and the published article had been revised by Prof. Dr. Wolfram Gronwald and
Prof. Dr. Peter Oefner.
Parts of section 5.1.2.10, pages 74, have already been published in [Zacharias et al. 2015] in a
slightly altered version. I performed both method implementation as well as data analyses. I
wrote the first draft of the manuscript and the published article had been revised by Prof. Dr.
Wolfram Gronwald and Prof. Dr. Peter Oefner.
3
Section 5.1.3.1, pages 74-76 has already been published in [Zacharias et al. 2015], and [Hochrein
et al. 2015] in a slightly altered version. The presented strategy to choose the appropriate data
normalization method was developed by Dipl. Math. Jochen Hochrein and is also part of his
Ph.D. thesis [Hochrein 2016]. I performed data analyses and wrote the first draft of [Zacharias
et al. 2015], and the published article had been revised by Prof. Dr. Wolfram Gronwald and
Prof. Dr. Peter Oefner.
Section 5.1.3.2, page 76, has already been published in [Zacharias et al. 2013a)] in a slightly
altered version. I performed data analyses and wrote the first draft of the manuscript, and
the published article had been revised by Prof. Dr. Wolfram Gronwald and Prof. Dr. Peter
Oefner.
Parts of section 5.1.3.3, pages 77-80, have already been published in [Zacharias et al. 2015] in
a slightly altered version. MS measurements and corresponding data analyses were performed
by M.Sc. Franziska Vogl. I performed all measurements and data analyses corresponding to
NMR spectroscopy.
Parts of section 5.1.3.4, pages 80-81, section 5.1.3.5, pages 81-82, and section 5.1.3.6, pages
83-84 have already been published in [Zacharias et al. 2015] in slightly altered versions. I
performed data analyses and wrote the first draft of the manuscript and the published article
had been revised by Prof. Dr. Wolfram Gronwald and Prof. Dr. Peter Oefner. I generated Fig.
5.2, page 84, and Fig. 7.3, page 146, and they were reprinted with permission from [Zacharias
et al. 2015]. Copyright 2015 American Chemical Society.
Section 5.1.4, pages 87-89, has already been published in [Zacharias et al. 2015], and [Hochrein
et al. 2015] in a slightly altered version. I wrote the first draft of [Zacharias et al. 2015] and
the published article had been revised by Prof. Dr. Wolfram Gronwald and Prof. Dr. Peter
Oefner.
Appendix II section 7.2.2, page 135, has already been published in [Zacharias et al. 2015]. I
wrote the first draft of [Zacharias et al. 2015] and the published article had been revised by
Prof. Dr. Wolfram Gronwald and Prof. Dr. Peter Oefner.
4
Danksagung
Hiermit bedanke ich mich herzlichst bei allen, die mir während dieser Doktrorarbeit mit Rat
und Tat zur Seite standen:
Vielen herzlichen Dank an Prof. Dr. Wolfram Gronwald, der mir die Möglichkeit gab, solch in-
teressante Projekte während meiner Doktorarbeit zu bearbeiten sowie für seinen fortwährende,
freundliche Anleitung.
Prof. Dr. Peter J. Oefner danke ich für die Möglichkeit, meine Doktorarbeit an seinem Institut
zu schreiben und für seine konstruktive Anleitung.
Prof. Dr. Kai-Uwe Eckardt, Prof. Dr. Carsten Willam, Dr. Gunnar Schley, und Dr. Stephanie
Titze vom Universitätsklinikum Erlangen-Nürnberg danke ich für die fruchtbaren Kollabora-
tionen.
Außerdem möchte ich mich bei Prof. Dr. Kai-Uwe Eckardt sowie Prof. Dr. Werner Kremer
für ihr Mentoring während meiner Doktorarbeit herzlichst bedanken.
Mein besonderer Dank geht an Dipl. Math. Jochen Hochrein für seine Unterstützung und Un-
terweisung im Bezug auf Algorithmenentwicklung sowie für die erfolgreiche Zusammenarbeit.
M.Sc. Franziska Vogl danke ich für ihre Massenspektrometriemessungen, ohne die ich vielleicht
immer noch über ein einzelnes NMR-Signal grübeln würde. Herzlichsten Dank geht an Clau-
dia Samol für ihre Hilfe im Labor und das Pipettieren wirklich unzähliger Proben. Außerdem
möchte ich mich bei Dr. Matthias Klein für seine Anleitung und Hilfe bei der theoretischen
und praktischen Arbeit, sowie bei Prof. Dr. Rainer Spang, Dr. Claudio Lottaz und Dr. Guisi
Moffa für ihre Beratung in statistischen Fragen bedanken.
Großen Dank möchte ich meinen Bürokollegen Dipl. Math. Jochen Hochrein, M.Sc. Philipp
Schwarzfischer, Dr. Matthias Klein, Dr. Claudio Lottaz, Dr. Matthias Maneck, M.Sc. Chris-
tian Kohler, Dr. Mohammad Sadeh, Dr. Paula Perez-Rubio, und Dr. Michael Altenbuchinger
für die nette Arbeitsatmosphäre aussprechen.
Vor allem jedoch möchte ich mich, neben den bereits oben erwähnten, bei allen weiteren Mit-
gliedern des Instituts für funktionelle Genomik für die tolle Zusammenarbeit und Gemeinschaft
bedanken: Dr. Martin Almstetter, Dr. Inka Appel, Dr. Nadine Aßmann, M.Sc. Raffaela
Berger, Sabine Botzler, Dr. Katja Dettmer-Wilde, Lisa Ellmann, Eva Engl, Dr. Julia En-
gelmann, Dr. Mauritz Evers, Corinna Feuchtinger, M.Sc. Franziska Görtler, Dr. Daniela
Herold, Dr. Bianca Höfelschweiger, Dr. Christian Hundsrucker, B.Sc. Elena Kremen, B.Sc.
Sebastian Mehrl, Dr. Katharina Meyer, Dipl. Bioinf. Anton Moll, Sandra Nothmann, Nadine
Nürnberger, Elke Perthen, Dipl. Math. Martin Pirkl, B.Sc. Sandra Rauh, M.Sc. Thorsten
Rehberg, Dr. Joerg Reinders, Dr. Yvonne Reinders, Dipl. Humanbiol. Sophie Schirmer, Dr.
Inga Schlecht, M.Sc. Trixi von Schlippenbach, M.Sc. Johann Simbürger, Dipl. Bioinf. Frank
Stämmler, Dr. Nicholas Strieder, Dipl. Bioinf. Franziska Taruttis, Dipl. Biol. Anja Thomas,
5
Dr. Monica Totir, Dr. Christian Wachsmuth, Dr. Magdalena Waldhier, B.Sc. Martha Wies-
mann, und Dr. Wentao Zhu.
Ein besonderer Dank geht an meine Freunde an der Universität Regensburg für den Zusam-
menhalt über so viele Jahre.
Ebenso möchte ich meinem Freund Michael für seine Unterstützung und sein Verständnis
danken, sowie für die zahlreichen schönen Stunden in Regensburg, München, Hermannsöd,
Florenz, Mailand, Brüssel, Antwerpen, Dresden, Venedig und London.
Zuletzt möchte ich meinen Eltern für ihre immerwährende Unterstützung und Liebe danken,
ohne die ich wohl nicht so weit gekommen wäre.
6
Contents
1 Abstract 13
2 Zusammenfassung 15
3 Introduction 17
3.1 Motivation: The global burden of kidney disease . . . . . . . . . . . . . . . . . . 17
3.2 Objective: Metabolomics in the context of nephrology . . . . . . . . . . . . . . . 19
4 Background 23
4.1 Introduction to nephrology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.1.1 Renal structure and physiology . . . . . . . . . . . . . . . . . . . . . . . 23
4.1.2 Clinical diagnostic tools for assessment of renal performance . . . . . . . 26
4.1.3 Basic concepts of acute kidney injury after cardiac surgery . . . . . . . . 28
4.1.4 Basic concepts of chronic kidney disease . . . . . . . . . . . . . . . . . . 32
4.1.5 Clinical study design in nephrology . . . . . . . . . . . . . . . . . . . . . 33
4.2 Fundamentals of nuclear magnetic resonance spectroscopy . . . . . . . . . . . . 36
4.2.1 The theory of nuclear magnetic resonance spectroscopy . . . . . . . . . . 36
4.2.2 General data acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.3 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.3.1 Statistical data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.3.2 Metabolite identification . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.3.3 Metabolite quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5 Biomedical Applications 63
5.1 Acute Kidney Injury study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.1.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.1.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.1.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5.2 German Chronic Kidney Disease study . . . . . . . . . . . . . . . . . . . . . . . 90
5.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5.2.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
5.2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.3 Trial to Reduce Cardiovascular Events with Aranesp® Therapy study . . . . . . 115
5.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
7
Contents
5.3.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
6 Conclusion and Perspectives 125
7 Appendix 129
7.1 Appendix I: General R-Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
7.1.1 Get familiar with data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
7.1.2 Choose normalization method . . . . . . . . . . . . . . . . . . . . . . . . 129
7.1.3 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
7.1.4 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
7.1.5 NMR bucket alignment and bucket fusion . . . . . . . . . . . . . . . . . 132
7.2 Appendix II: Acute Kidney Injury study . . . . . . . . . . . . . . . . . . . . . . 133
7.2.1 Clinical characteristics and outcome of patients included in AKI study . 133
7.2.2 CPB protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
7.2.3 Spike-In experiments for the quantification of free calcium and magne-
sium levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
7.2.4 Time-course development . . . . . . . . . . . . . . . . . . . . . . . . . . 137
7.2.5 Results of permutation tests . . . . . . . . . . . . . . . . . . . . . . . . . 138
7.2.6 Discriminative 24 h plasma NMR features . . . . . . . . . . . . . . . . . 139
7.3 Appendix III: German Chronic Kidney Disease Study . . . . . . . . . . . . . . . 148
7.3.1 Patient characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
7.3.2 t-tests between various leading renal diseases . . . . . . . . . . . . . . . . 151
7.3.3 Prediction of present and future kidney performance . . . . . . . . . . . 222
7.4 Appendix IV: Trial to Reduce Cardiovascular Events with Aranesp® Therapy
study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238
8 About the author 253
8.1 Curriculum Vitae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
8.2 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
8.3 Poster Presentations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
8.4 Conference Talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
9 Bibliography 255
8
Abbreviations
1D one-dimensional
2D two-dimensional
ACE angiotensin-converting-enzyme
ACR albumin-creatinine ratio
ADPKD autosomal dominant polycystic kidney disease
AER albumin excretion rate
AKI acute kidney injury
AKIN Acute Kidney Injury Network
AP alkaline phosphatase
ARF acute renal failure
a. u. arbitrary units
AUC area under the curve
B/H Benjamini/Hochberg
BMI body mass index
BSA body surface area
CABG coronary artery bypass grafting
CGA Cause, GFR, and Albuminuria
ChEBI Chemical Entities of Biological Interest
CKD chronic kidney disease
CKD-EPI Chronic Kidney Disease Epidemiology Collaboration
CKD-EPI crea CKD-EPI formula based on SCr
CKD-EPI crea cys CKD-EPI formula based on SCr and SCysC
CKD-EPI cys CKD-EPI formula based on SCysC
CKD-JAC Chronic Kidney Disease Japan Cohort
COPD chronic obstructive pulmonary disease
COSY Correlated Spectroscopy
CPB cardiopulmonary bypass
CPMG Carr-Purcell-Meiboom-Gill
CRIC Chronic Renal Insufficiency Cohort
CRP C-reactive protein
CysC cystatin C
EDTA ethylenediaminetetraacetic acid
eGFR estimated glomerular filtration rate
eGFR eGFR based on CKD-EPI crea formula
ckdepicrea
eGFR eGFR based on CKD-EPI crea cys formula
ckdepicreacys
9
eGFR eGFR based on CKD-EPI cys formula
ckdepicys
eGFR eGFR based on MDRD4 formula
mdrd4
EPO erythropoietin
ESA erythropoiesis-stimulating agent
ESI electrospray ionization
ESKD End Stage Kidney Disease
ESRD end-stage renal disease
FDR false discovery rate
FFP fresh frozen plasma
FID free induction decay
FU2 second follow-up
GCKD German Chronic Kidney Disease
GFR glomerular filtration rate
GGT γ-glutamyltranspeptidase
Hb hemoglobin
HbA glycated hemoglobin
1c
HF hemofiltration
HMBC heteronuclear multiple bond correlation
HMDB Human Metabolome Database
HPLC high-performing liquid chromatography
HSQC heteronuclear single quantum coherence
IABP intra-aortic balloon pump
ICU intensive care unit
IL-18 interleukin-18
INEPT insensitive nuclei enhanced by polarization transfer
KDIGO Kidney Disease: Improving Global Outcomes
KIM-1 kidney injury molecule-1
LARS least-angle regression
LASSO least absolute shrinkage and selection operator
LC-MS liquid chromatography-mass spectrometry
LTB leukotriene B
4 4
MAP mean arterial pressure
MDRD Modification of Diet in Renal Disease
MDRD4 four-variable Modification of Diet in Renal Disease
MS mass spectrometry
mse mean-squared error
NAG N-acetyl-β-D-glucosaminidase
NGAL neutrophil-gelatinase-associated lipocalin
NMR nuclear magnetic resonance
NOE Nuclear Overhauser Effect
NOESY Nuclear Overhauser Enhancement Spectroscopy
NSAID nonsteroidal anti-inflammatory drugs
OPLS-DA orthogonal projection to latent structures discriminant analysis
10