influence of bone-associated and cardiovascular biomarkers

Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine 1785

Influence of bone-associated and cardiovascular biomarkers on vascular events and mortality in relation to renal dysfunction

PING-HSUN WU

ACTA UNIVERSITATIS

UPSALIENSIS UPPSALA

2021

ISSN 1651-6206 ISBN 978-91-513-1336-8 URN urn:nbn:se:uu:diva-456907

Dissertation presented at Uppsala University to be publicly examined in Enghoffsalen, Ing 50 bv, Akademiska sjukhuset, Uppsala, Wednesday, 15 December 2021 at 09:00 for the degree of Doctor of Philosophy (Faculty of Medicine). The examination will be conducted in English. Faculty examiner: Associate Professor Jonas Spaak (Cardiology at the Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet).

Abstract Wu, P.-H. 2021. Influence of bone-associated and cardiovascular biomarkers on vascular events and mortality in relation to renal dysfunction. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine 1785. 63 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-513-1336-8.

Biomarkers can help physicians identify subjects with an increased cardiovascular risk. Apart from the clinical factors, some biomarkers have been recognized as important predictors and risk factors for cardiovascular disease in renal disease. The applicability of biomarkers may be limited in patients with kidney disease due to the complex etiology of cardiovascular disease, which warrants separate evaluations, including established and novel biomarkers. The overall aim of the thesis was to investigate the association between bone-associated markers and cardiovascular proteins on death and vascular events in the elderly male population and patients with kidney disease.

Study I included 3,014 participants in Swedish multicenter prospective Osteoporotic Fractures in Men (MrOS) cohort and investigated the associations between Klotho single-nucleotide polymorphism and mortality. Two potentially damaging single-nucleotide polymorphisms (rs9536314 and rs9527025) in the Klotho gene were not associated with mortality.

Study II investigated the association between mineral bone markers and all-cause mortality / cardiovascular mortality. The composite evaluation of elevated fibroblast growth factor-23 levels, vitamin D deficiency, and renal impairment was associated with mortality.

Study III evaluated the bone-associated proteins and mortality/composite vascular events in the 331 Demark hemodialysis patients. Osteoprotegerin, as one of the most promising bone-related proteins, was associated with composite vascular events independent of cytokine.

Study IV investigated the association between 92 proteins measured by proximity extension assay and mortality/composite vascular events in hemodialysis patients. A higher level of Interleukin-8, T-cell immunoglobulin and mucin domain 1, C-C motif chemokine 20, and lower level of stem cell factor and galanin peptides were associated with poor outcomes.

This thesis addressed the issue of bone-vascular axis and cardiovascular disease. We evaluated from gene levels to circulating protein levels and from the general population to patients with kidney disease. Based on our research findings, more evidence was linked between bone and vascular complications. We also identified several cardiovascular proteins considered potentially important predictors for cardiovascular disease in patients with renal failure, especially hemodialysis patients.

Keywords: biomarkers, bone markers, cardiovascular markers, proteomics, vascular event, mortality, renal function, hemodialysis

Ping-Hsun Wu, Department of Medical Sciences, Endocrinology and mineral metabolism, Akademiska sjukhuset, Uppsala University, SE-751 85 Uppsala, Sweden.

© Ping-Hsun Wu 2021

ISSN 1651-6206 ISBN 978-91-513-1336-8 URN urn:nbn:se:uu:diva-456907 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-456907)

http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-456907

To my lovely family…... you give me the roots to stand tall and strong

List of Papers

This thesis is based on the following papers, which are referred to in the text

by their Roman numerals.

I Ping-Hsun Wu, Per-Anton Westerberg, Andreas Kindmark, Åsa

Tivesten, Magnus K Karlsson, Dan Mellström, Claes Ohlsson,

Bengt Fellström, Torbjörn Linde, Östen Ljunggren. (2020)

The association between Single Nucleotide Polymorphisms of

Klotho Gene and Mortality in Elderly Men: The MrOS Sweden

Study. Sci Rep, Jun 24;10(1):10243; doi: 10.1038/s41598-020-

66517-5

II Ping-Hsun Wu, Per-Anton Westerberg, Magnus K. Karlsson,

Dan Mellström, Torbjörn Linde, Bengt Fellström, Östen Ljung-

gren. The effect of fibroblast growth factor 23, vitamin D, and

renal function on all-cause and cardiovascular mortality: The

MrOS Sweden Study. (Manuscript)

III Ping-Hsun Wu, Rie Io Glerup, Jeppe Hagstrup Christensen, My

Hanna Sofia Svensson, Torbjörn Linde, Östen Ljunggren, Bengt

Fellström.(2021) Osteoprotegerin predicts cardiovascular events

in patients treated with hemodialysis. Nephrol Dial Transplant,

Jun 4;gfab192. doi: 10.1093/ndt/gfab192.

IV Ping-Hsun Wu*, Rie Io Glerup*, My Hanna Sofia Svensson,

Niclas Eriksson, Jeppe Hagstrup Christensen, Philip de Laval,

Inga Soveri, Magnus Westerlund, Torbjörn Linde, Östen Ljung-

gren, Bengt Fellström. Novel biomarkers detected by proteomics

predict death and cardiovascular events in hemodialysis patients.

(Submitted)

*Authors contributed equally to this work

Reprints were made with permission from the respective publishers.

Contents

Introduction ................................................................................................... 11 Cardiovascular disease in patients with chronic kidney disease .............. 11 Chronic kidney disease and mineral bone disease ................................... 12 Biomarkers in patients with chronic kidney disease ................................ 12 Vascular calcification and cardiovascular disease ................................... 13 Bone-vascular axis ................................................................................... 13 Bone markers in patients with kidney disease .......................................... 15 Protein biomarkers and cardiac structure and function ............................ 15

Proteomics technology .................................................................................. 17 Brief introduction of proteomics .............................................................. 17 Biotechnology of clinical proteomics using proximity extension assays . 18

Aims .............................................................................................................. 19 General aim .............................................................................................. 19 Specific aims of the studies ...................................................................... 19

Study samples ............................................................................................... 21 The Swedish part of the Osteoporotic Fractures in Men Study (MrOS) .. 21 Denmark Århus Hemodialysis Cohort ..................................................... 21

Methods ........................................................................................................ 22 Study designs and methods ...................................................................... 22 Biomarker measurements ......................................................................... 27 Outcomes definitions and mortality assessment ....................................... 29 Statistical analysis .................................................................................... 30

Main results ................................................................................................... 33 Study I ...................................................................................................... 33 Study II ..................................................................................................... 33 Study III ................................................................................................... 35 Study IV ................................................................................................... 36

Additional Preliminary Study ....................................................................... 37 Method and Statistical analysis of the additional preliminary study ........ 37 Preliminary results of additional study ..................................................... 38

Discussion ..................................................................................................... 42

Klotho gene SNPs and all-cause or CV mortality .................................... 42 The mineral bone factors and all-cause or CV mortality ......................... 43 Bone-associated markers and mortality or CVE in hemodialysis

patients ..................................................................................................... 45 CV-associated markers for mortality or CVE in hemodialysis patients ... 46 Mortality differences among hemodialysis patients between Asian and

Caucasian ................................................................................................. 48 Strengths and limitations .......................................................................... 50

Conclusion and future perspectives .............................................................. 51

Summary in Swedish (sammanfattning på svenska) ..................................... 53

Acknowledgments......................................................................................... 54

References ..................................................................................................... 55

Abbreviations

AMI Acute myocardial infarction

CAD Coronary artery disease

CCL20 C-C motif chemokine 20

CKD Chronic kidney disease

CKD-BMD Chronic kidney disease and mineral bone disease

CRP C-reactive protein

CTSD Cathepsin D

CTSL1 Cathepsin L1

CV Cardiovascular

CVD Cardiovascular disease

CVE Composite vascular event

DKK-1 Dickkopf-related protein 1

eGFR Estimated glomerular filtration rate

ESKD End-stage kidney disease

FGF23 Fibroblast growth factor 23

GAL Galanin peptides

HGF Hepatocyte growth factor

IL-8 Interleukin-8

IDI Integrated discrimination improvement

NRI Net reclassification improvement

OPG Osteoprotegerin

PAD Peripheral artery disease

PCR Polymerase chain reaction

PEA Proximity extension assay

PTH Parathyroid hormone

RANKL Receptor activator of nuclear factor κB ligand

RF Random forest

SCF Stem cell factor

SNP Single nucleotide polymorphism

TIA Transient ischemic attack

TIM-1 T-cell immunoglobulin and mucin domain 1

TRAIL TNF-related apoptosis-inducing ligand

TRAIL-R2 TNF-related apoptosis-inducing ligand receptor 2

VSMC Vascular smooth muscle cells

11

Introduction

Cardiovascular disease in patients with chronic kidney

disease

Chronic kidney disease (CKD) is a global public health problem associated

with high morbidity and mortality. Cardiovascular disease (CVD) is the major

cause of premature death in patients with CKD.1, 2 Death from CVD is far

more common in patients with CKD than progression to end-stage kidney dis-

ease (ESKD).3 Chronic hemodialysis treatment is associated with a risk of

premature mortality that is 10–50 times higher than that in the age-matched

general population, with CVD being a leading cause of death.4 Large prospec-

tive cohort studies have documented significant associations of worse kidney

function and proteinuria with CVD and mortality that are independent of tra-

ditional risk factors.2, 5, 6 CKD has been recognized as an independent risk fac-

tor for CVD and has now been recognized as a coronary artery disease (CAD)

risk equivalent,7 similar to diabetes mellitus, suggesting that the risk from

CKD is equivalent to that from established CAD. Population-based studies in

Asia have also established CKD as a substantial risk factor for CVD and all-

cause mortality, suggesting CKD is a serious public health issue that trans-

cends ethnicity.8-10 The prevalence of CVD in patients 66 years of age and

older who have CKD is 68.8% compared with 34.1% in those who do not have

CKD. The complex relationship between CVD and kidney disease is thought

to be attributable to shared traditional risk factors (e.g., diabetes mellitus, hy-

pertension, physical inactivity, left ventricular hypertrophy, smoking, family

history, and dyslipidemia) as well as to the influence of nontraditional risk

factors in the presence of CKD (e.g., endothelial dysfunction; vascular medial

hyperplasia, sclerosis, and calcification; volume overload; abnormalities in

mineral metabolism; anemia and malnutrition; inflammation; oxidative stress;

hyperparathyroidism, hyperhomocysteinemia, and autonomic imbalance).

Cardio-renal syndrome continues to pose both diagnostic and therapeutic chal-

lenges for those with heart failure.11 Thus, CVD is an important comorbidity

for patients with CKD.

12

Chronic kidney disease and mineral bone disease

CKD is characterized by mineral and bone disorders associated with ex-

traosseous and cardiovascular (CV) calcifications. Experimental studies and

clinical observations in the general population and in patients with CKD show

an inverse relationship between the extent of CV calcifications and bone min-

eral density or bone metabolic activity. Hyperphosphatemia, reduced Klotho

expression and impaired soft-tissue calcification defenses are key pathophys-

iologic components of chronic kidney disease and mineral bone disease

(CKD–MBD). In patients receiving dialysis, the coronary artery calcification

score was found to be inversely correlated with vertebral bone mass.12 A high

systemic calcification score combined with bone histomorphometry sugges-

tive of low bone activity was observed in patients receiving hemodialysis.13

Disturbances of the fibroblast growth factor-23 (FGF23)/alpha-Klotho

(Klotho) axis in CKD are related to CKD-associated vascular calcification.

The decrease in Klotho expression in CKD might provide several explanations

for the link between the uremic state and vascular calcification. Thus, admin-

istration of protein-bound uremic toxin to mice induced not only vascular cal-

cification,14 but also a decrease in tubular Klotho expression subsequent to

Klotho gene hypermethylation (“epigenetic modification”).15

Biomarkers in patients with chronic kidney disease

Biomarkers that will help to improve the identification of patients at risk of

CV events has been at the core of extensive research in the general population

and in patients with CKD.16 The predicate of that approach is that an accurate

early assessment of CV risk will facilitate more aggressive and focused treat-

ment of those in greater need of preventive measures to reduce event rates.

Several important clinical CVD risk factors for patients with CKD have been

established.17 In addition, other biomarkers representing potential risk factors

for CVD in patients with CKD have also been identified: high-sensitivity C-

reactive protein (hsCRP),18 fibroblast growth factor 23 (FGF23),19, 20 and N-

terminal pro-brain natriuretic peptide (NT-proBNP),21 among others. Cardiac

troponin T is cardiac-specific and is not present in serum after non-myocardial

muscle damage. Detecting asymptomatic CAD, especially multi-vessel dis-

ease, as a predictor of mortality in CKD patients could be helpful. However,

as in the case of several other markers, variability of the test for troponin T

both day-to-day and from one laboratory to another could limit the possibility

of issuing widely applicable guidelines for using this biomarker in CKD.22

Other biomarkers, such as calciprotein particles, sclerostin or Dickkopf-re-

lated protein 1 (DKK-1), Neutrophil Gelatinase-Associated Lipocalin, plasma

growth differentiation factor-15, asymmetric dimethylarginine require more

13

study before their roles as key mediators of CV damage and potential thera-

peutic targets in patients with CKD can be elucidated.22 Given the extremely

high CV risk and complex routes of CVD development in patients with CKD,

new biomarkers and prediction based on composite biomarkers are worthy of

investigation.

Vascular calcification and cardiovascular disease

Individuals with vascular calcification present the most significant risk of pro-

gressive CVD. The dramatically increased CV risk in patients with CKD is

directly related to vascular calcification.23 Calcification of the intima is a part

of atherosclerosis, while medial calcification is the hallmark of arteriosclero-

sis. Vascular calcification is a multi-step process that includes supersaturated

phosphate and calcium precipitation in the extracellular milieu and cell-medi-

ated processes, such as apoptosis, osteochondrogenic differentiation, and elas-

tin degradation.24 This phenotypic conversion of vascular smooth muscle cells

into an osteoblast-like phenotype is induced by osteoblast transcriptional ac-

tivation of osteocyte-specific proteins, such as FGF23 and sclerostin through

a Wnt-dependent mechanism.25 In vascular smooth muscle cells, transdiffer-

entiation and osteogenic lineage allocation of multipotent mesenchymal pro-

genitors can generate osteogenic progenitors. Metabolic and inflammatory in-

sults affect vascular function, causing vascular mesenchymal cells to develop

into osteogenic cells, and impair normal vascular smooth muscle cell func-

tion.26

Animal studies demonstrated that Klotho protects the vasculature against cal-

cification in CKD.27 Disturbances of the FGF23/ Klotho axis are related to

CKD-associated vascular calcification. In addition to Klotho, several other in-

hibitors of vascular calcification have been identified: inorganic substances

such as pyrophosphate and magnesium, and peptidic substances such as fe-

tuin-A, osteoprotegerin (OPG), and bone morphogenetic protein 7.28

Bone-vascular axis

Clinical and experimental studies suggested a bone–vascular axis because of

a link between bone and artery remodeling and alterations.26 Although the

physiologic link between vascular illness and bone alterations is undoubtedly

part of the aging process, several studies found that bone–vessel associations

remained significant after adjusting for age, suggesting that the causative re-

lationship is age-independent. Nevertheless, the factors or mechanisms under-

lying those associations are not well understood. They might reflect common

14

mechanisms acting on both systems (e.g., inflammation, hyperlipidemia, dia-

betes, smoking, or generalized arterial disease per se). A bidirectional endo-

crine connection between bone and the vasculature mutually benefits bone and

vascular health.26 The kidney plays a crucial role in the process by regulating

phosphate excretion and Klotho expression. Hyperphosphatemia, reduced

Klotho expression, and impaired soft tissue calcification defenses are key

pathophysiologic components of chronic kidney disease and mineral bone dis-

ease (CKD-MBD).29 Other essential factors in bone and vascular metabolism

include proteins such as the bone morphogenetic proteins, receptor activator

of nuclear factor κB ligand [RANKL], OPG, and matrix Gla protein; parathy-

roid hormone; phosphate; oxidized lipids; and vitamins D and K.

Numerous clinical investigations have now proved the link between osteopo-

rosis and vascular calcification, and the increased risk of CVD in patients with

osteoporosis (Figure 1). However, compared with placebo in the FREEDOM

(Fracture Reduction Evaluation of Denosumab in Osteoporosis every 6

Months) trial, denosumab (a monoclonal antibody against RANKL that im-

proves bone marrow density and reduces fracture risk) did not affect the pro-

gression of aortic calcification or the incidence of CV adverse events.30 Alt-

hough the relationship between OPG/RANK/RANKL and vascular calcifica-

tion is not yet fully elucidated, further studies focus on RANKL inhibitor and

CV outcomes.

Figure 1. The Bone-vascular axis and cardiovascular complications

15

Bone markers in patients with kidney disease

The human body remodels (turns over) bone at both cortical and cancellous

sites. Bone remodeling is regulated both by systemic factors (e.g., parathyroid

hormone [PTH], phosphorus, 1,25-dihydroxyvitamin D, circulating scle-

rostin, and perhaps FGF23) and by local microenvironmental factors

(RANKL, OPG, and sclerostin). Several biochemical markers of bone turno-

ver can be measured in serum, including bone resorption and bone formation

markers. Data suggest that serum levels of specific bone turnover markers

might help discriminate the various forms of renal bone disease. Those serum

marker levels are valuable in assessing systemic bone turnover in osteoporosis

and in assessing the body’s response to antiresorptive agents (which inhibit

bone turnover) and anabolic agents (which stimulate bone turnover).31-33 Bio-

chemical markers of bone turnover—in particular, serum PTH and bone-spe-

cific alkaline phosphatase—might be able to differentiate between biopsy-

proven adynamic renal bone disease, hyperparathyroid bone disease, and os-

teomalacia.

The baseline bone turnover markers sclerostin and tartrate-resistant acid phos-

phatase 5b are independent predictors of bone loss in patients receiving dial-

ysis.34 However, investigations are still required to use noninvasive markers

of bone resorption in patients with uremia. Moreover, the dynamic marker of

bone mass density requires an observation period of 6 months to 1 year be-

tween measurements. In contrast, metabolic markers accurately reflect the

state of bone metabolism at the time of measurement. . When there is doubt

about choosing a drug for pharmacotherapy, metabolic markers can help to

suggest the appropriate selection. Furthermore, in evaluating the effects of

drug therapy on disease improvement, an assessment of bone metabolism at

the time of diagnosis is recommended whenever possible.

Recently, the clinical application of bone metabolic markers has progressed

significantly, and measurements of the accepted indices provide a better un-

derstanding of pathologic osteoporosis. Guidance for the application and as-

sessment of bone metabolic markers in clinical practice is therefore crucial.

Protein biomarkers and cardiac structure and function

There is evidence that impairment of left ventricular diastolic relaxation de-

velops in concert with activation of circulating plasminogen activator inhibi-

tor-1.35 Myocardial fibrosis is also a significant determinant of diastolic func-

tion. The plasma level of the carboxy-terminal peptide of procollagen type I

has thus been suggested as a valuable marker for determining the level of fi-

16

brotic activity36, 37 and diastolic dysfunction.38 Hyperphosphatemia can de-

plete Klotho levels and increase FGF-23, which can directly contribute to car-

diac hypertrophy and fibrosis, and vascular calcification.11 Moreover, in ani-

mal models, the soluble inflammation and fibrosis biomarker galectin-3 has

been shown to have a causative role in renal and cardiac fibrosis, potentially

representing common pathophysiology for progression toward ESKD and car-

diomyopathy.39 Galectin-3 is implicated in the pathogenesis of fibrosis in the

heart, but it also increases with normal aging and renal impairment.40 Galec-

tin-3 has also been shown to promote transforming growth factor-β–mediated

activation of fibroblasts in the kidney.41 Thus, a renal–cardiac interaction

could be observed.

Accumulating evidence has proved that several noninvasive markers are prog-

nostic indicators in patients with CKD. But head-to-head comparison studies

between echocardiography and other noninvasive markers in patients with

CKD are limited. Biomarkers cannot replace echocardiography, but might

play a complementary role in evaluating CV risk in CKD. Exploring the link

between proteomics and cardiac structure and function is an excellent way to

begin to understand the pathophysiology of cardiac remodeling in uremia-as-

sociated cardiomyopathy.

17

Proteomics technology

Brief introduction of proteomics

All cells, organs, and organisms contain proteins, which are polymers of

amino acids. All proteins that can be measured in a biological sample are con-

sidered part of the proteome.42 Proteomics is a technique for identifying pro-

teins and determining their quantity, modifications, and interactions. Prote-

omics data usually reveal information about the biological function, which is

crucial in metabolic processes and illnesses.43 Proteins may be identified and

quantified using various analytical methods, including liquid chromatog-

raphy-tandem mass spectrometry, isotope-coded protein labeling, and protein

microarrays. Mass spectrometry has been regarded as a "Gold Standard"

method for identifying and analyzing individual proteins in expression prote-

omics research. However, mass spectrometry is time-consuming and expen-

sive compared to immunoaffinity assay. The choice of an affinity-based ap-

proach or mass spectrometry for a given experiment is influenced by several

factors. Affinity-based approaches have the following advantages: (a) high

sensitivity for numerous low-abundance proteins, (b) high sample throughput,

(c) ease of use, (d) modest instrument investment, and (e) ability to target spe-

cific proteins of biological or clinical relevance that have been defined a priori.

However, limitations include (a) the inability to identify proteins not targeted

by the assay, (b) the inability to distinguish posttranslational modifications

and isoforms, (c) the potential influence of coding DNA variants on epitope

structures and reagent affinity, (d) batch effects, which can cause significant

variability in reagents and irreproducibility, and (e) the lack of open science

platforms for several of the available methods.44

Using immunoaffinity assays, such as proximity extension assays (PEA), we

may now investigate many proteins simultaneously. Cross-reactivity, which

prevents multiplexing, specificity difficulties, and inadequate sensitivity to

identify proteins in the lower abundance spectrum, has been criticized in im-

munoaffinity tests.45 Proximity extension assays are nucleotide-labeled immu-

noassays that have been proven to identify proteins with excellent specificity

in small sample quantities.45 Due to the requirement of dual recognition of a

target protein by a matched pair of DNA-conjugated antibodies, the proximity

extension test has high specificity.

18

Biotechnology of clinical proteomics using proximity extension assays

A PEA is a 96-plex immunoassay for high-throughput detection of protein

biomarkers in liquid samples. A matched pair of antibodies linked to unique

oligonucleotides (“proximity probes”) binds to the respective protein target

for each biomarker. In the original assay design, one PEA probe consisted of

a double-stranded oligonucleotide attached to the antibody at its 3′ end, with

a 9 or so nucleotide 3′ overhang at the 5′ end. That overhang was complemen-

tary to the 3′ end of the oligonucleotide bound to the other antibody partner.

After incubation of the proximity probes with a sample containing the antigen

recognized by the probes, the overhanging 3′ end could hybridize to the 5′

oligonucleotide, and after the addition of a DNA polymerase, the free 3′ OH

was extended in the 5′–3′ direction toward the attachment site of the 5′ oligo-

nucleotide thereby generating a full-length amplicon and hybridization site for

the upstream primer. Thus, amplification and detection of the target antigen

by polymerase chain reaction (PCR) were facilitated. A modification has now

replaced that arrangement.

Each of the 2 single-stranded oligonucleotides contains a complementary site

for pair-wise annealing with the other oligonucleotide, allowing for an exten-

sion by a DNA polymerase and eliminating the requirement for a double-

stranded oligonucleotide with a 3′ overhang. Furthermore, a reasonable choice

of DNA polymerase minimizes background noise and improves the assay’s

sensitivity. Each of the 96 oligonucleotide antibody pairs contains unique

DNA sequences, allowing for a hybridization only to each other. Subsequent

proximity extension will create 96 unique DNA reporter sequences, which are

amplified by real-time PCR.

A limiting factor of multiplexed immunoassays is antibody cross-reactivity,

which restricts the degree of multiplexing of most assays to below 10-plex.

Olink panels will not detect cross-reactive events, given that only matched

DNA reporter pairs are amplified with real-time PCR. The new approach al-

lows for scalable multiplexing without loss of specificity and sensitivity. The

addition of a DNA polymerase, therefore, leads to an extension of the hybrid-

izing oligo, bound to one of the probes, creating a DNA amplicon that can

subsequently be detected and quantified by quantitative real-time PCR.45, 46

19

Aims

General aim

The overall aim of this thesis was to investigate the association of bone-asso-

ciated biomarkers and CV proteins with death and vascular events in the gen-

eral population and patients with kidney disease (Figure 2).

Specific aims of the studies

The aim of Study I was to investigate the associations between Klotho single-

nucleotide polymorphisms (SNPs) and all-cause mortality or CV mortality in

Sweden multicenter prospective Osteoporotic Fractures in Men (MrOS) co-

hort.

The aim of Study II was to investigate abnormal levels of mineral bone mark-

ers (FGF23, vitamin D, and PTH) and all-cause mortality or CV mortality in

the MrOS cohort.

The aim of Study III was to evaluate bone-associated proteins and clinical

outcomes (mortality or composite vascular events [CVEs]) in Demark’s Århus

HD cohort. In addition, the effects of calcium, phosphate, PTH, and inflam-

mation cytokine on bone-associated protein were also investigated to evaluate

how they might improve the prediction of CVEs.

The aim of Study IV was to explore the associations of 92 protein biomarkers

(measured by PEA) with death and CVEs in the Denmark Århus HD cohort.

20

Figure 2. The overall and specific aim of the thesis: Bone-associated and CV markers– target on cardiovascular disease

21

Study samples

The Swedish part of the Osteoporotic Fractures in Men

Study (MrOS)

The prospective international MrOS study of osteoporosis and fracture risk

enrolled elderly male participants in the United States, Sweden, and Hong

Kong. We included Sweden’s MrOS participants 69–81 years of age. The par-

ticipants (n = 3014) were randomly selected from a national population data-

base, constituting three sub-cohorts in Uppsala (n = 999), Göteborg (n =

1010), and Malmö (n = 1005). Ethics committees in Sweden approved the

MrOS study at Uppsala (Ups 01-057), Göteborg (Gbg M 014-01), and Lund

(LU 693-00). The study was performed in accordance with the Declaration of

Helsinki. Informed consent was obtained from all study participants.

Denmark Århus Hemodialysis Cohort

The prospective multicenter Århus HD cohort study protocol was approved

by the Regional Research Ethics Committee of The North Jutland Region

(protocol N-20100041), and the study complied with the Declaration of Hel-

sinki. Patients receiving HD at 5 dialysis facilities in Jutland, Denmark, were

enrolled between December 2010 and March 2011, and follow-up continued

for 5 years. Patients receiving chronic HD who were less than 18 years of age

or who had acute kidney injury or an inability to understand informed consent

were excluded. Serum samples were collected immediately before a dialysis

session and were stored at –80°C for later analysis. One physician reviewed

medical records for all participants at study entry and during follow-up. Clin-

ical events recorded during follow-up included cause of death and CVEs (a

composite endpoint of acute myocardial infarction [AMI], unstable angina,

ischemic stroke, transient ischemic attack, hemorrhagic stroke, and peripheral

artery disease [PAD]). All patients were followed for up to 5 years, until death,

loss to follow-up, dialysis modality change, kidney transplantation, or HD

treatment cessation.

22

Methods

Study designs and methods

An overview of each of the Studies I–IV is presented in Table 1.

Table 1. Overview of the designs and methods for studies I–IV

Study I II III IV

Design Cohort study Cohort study Cohort study Cohort study

Subjects Total of 2921

male subjects

with Klotho

gene SNP and

follow-up rec-

ords in Sweden

MrOs cohort

Total of 3014

male subjects

with FGF23,

PTH, vitamin

D, and renal

function as

well as follow-

up records in

Sweden MrOs

cohort

Total of 331 he-

modialysis pa-

tients with 9

bone associated

protein markers

in Denmark År-

hus hemodialysis

cohort

Total of 331 he-

modialysis pa-

tients with 92

CV protein

markers in Den-

mark Århus he-

modialysis co-

hort

Expo-

sure

Klotho Gene

SNP

Mineral bone

markers

Bone markers CV biomarkers

Out-

come

All-cause mor-

tality

CV mortality

All-cause

mortality

CV mortality

All-cause mor-

tality

CV mortality

CVE

All-cause mor-

tality

CV mortality

CVE

Analysis Logistic regres-

sion

Cox regression

Cox regres-

sion

Cox regression Cox regression

23

The detailed study flowchart of each of the Studies I–IV is presented

below

Study I47

24

Study II

25

Study III48

26

Study IV

27

Biomarker measurements

Genotyping of the Klotho gene (Study I)

Using standard methods, DNA was extracted from the whole blood of 3014

study participants. Using the HaploView 4.2 tagger algorithm (Mark Daly lab,

Broad Institute, Cambridge, MA, U.S.A.), SNPs covering the klotho gene and

the flanking 5′ and 3′ regions were tagged.49 Genotyping was performed using

matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

on a Sequenom MassARRAY system (Sequenom Inc., Newton, MA, U.S.A.)

with iPLEX assay. The primers were designed using the MassARRAY Assay

Design software (version 3). PCR was performed according to the standard

iPLEX methodology.

Genotyping quality control was completed by excluding from the analysis in-

dividual samples or SNPs with genotype call rates below 95%, SNP assays

with poor-quality spectra or cluster plots, and SNPs that significantly deviated

from the Hardy–Weinberg equilibrium (p < 0.05). Genotype data for the SNPs

were pairwise calculated with respect to linkage disequilibrium and identifi-

cation of SNPs with the highest haplotype predictability. Successful genotyp-

ing was obtained for 23 SNPs, with an overall call rate of 98.9%. Allele fre-

quencies for those SNPs were calculated, and the Hardy–Weinberg equilib-

rium was tested for each SNP using chi-square goodness-of-fit statistics for

all but 2 SNPs (rs1888057 and rs2283368) in the cohort. The latter 2 SNPs

were subsequently excluded from further analyses.

HaploView 4.2 was accessed to generate haplotype blocks, linkage disequi-

librium values and diagrams, and SNPs tagged using the tagger algorithm.49

The data for chromosome location, minor genotype frequency, SNP function

and regulation of the human klotho gene, and wild or mutated residue of the

nonsynonymous SNPs were used according to program requirements.

FGF23 and renal function measurement (Study II)

Serum samples were collected and stored at -80° C. The serum concentration

of intact FGF23 was analyzed using ELISA (Kainos Laboratories Interna-

tional; Tokyo, Japan).50 The estimated glomerular filtration rate (eGFR) based

on cystatin C (Cystatin C Immunoparticles; Dako A/S, Glostrup, Denmark)

was calculated using the formula: eGFR cystatin C=79.901*(Cyst C [mg/L])-

1.4389. This proxy for eGFR shows a strong correlation with the iohexol clear-

ance rate (R2=0.956).51, 52

28

Proximity extension assay method (Study III and Study IV)

Bone-associated proteins and inflammatory cytokine proteins were assessed

using the Proseek Multiplex 96×96 PEA with the CVD I panel (Olink Biosci-

ence, Uppsala, Sweden). The assay simultaneously measures proteins using 2

particular antibodies for each protein, linking a PCR reporter sequence. Each

sample includes 2 incubations, 1 extension, and 1 detection control to deter-

mine the lower detection limit and to normalize the measurements on the Flu-

idigm BioMark HD real-time PCR platform. The 9 bone-associated proteins

investigated included cathepsin D (CTSD), cathepsin L1 (CTSL1), Dickkopf-

related protein 1, FGF23, leptin, OPG, RANKL, TNF-related apoptosis-in-

ducing ligand (TRAIL), and TNF-related apoptosis-inducing ligand receptor 2

(TRAIL-R2). In addition, all 92 CV proteins were evaluated for their associa-

tions with mortality and CV outcomes.

29

Outcomes definitions and mortality assessment

In Studies I and II, mortality was retrieved from Statistics Sweden. Follow-

up duration was recorded from the baseline visit (2001–2004) to the date of

death or last mortality data collection (March 1, 2008). Cause-of-death data

were recorded using International Classification of Diseases codes based on

death certificates obtained from the Swedish Cause of Death Register. For this

thesis, CV death was defined using codes I00–I99 from the International Clas-

sification of Diseases, 10th revision.

In Studies III and IV, medical records for all participants were reviewed by

1 physician at study entry and during follow-up. Clinical events recorded dur-

ing follow-up included cause of death and CVEs (a composite endpoint of

AMI, unstable angina, ischemic stroke, transient ischemic attack, hemorrhagic

stroke, and PAD). Individual CVEs were described as follows:

a. AMI was diagnosed when at least 2 of these 3 criteria were met: Pres-

ence of cardiac ischemic chest pain; electrocardiography findings

meeting the criteria for a definitive diagnosis during an event (new or

presumed new significant ST-segment–T wave changes, or new left

bundle branch block, or development of pathologic Q waves); ele-

vated cardiac enzymes measured during an event.

b. Unstable angina requiring hospitalization was defined as symptoms

of myocardial ischemia at rest (chest pain or equivalent) or an accel-

erating pattern of angina with frequent episodes associated with pro-

gressively decreased exercise capacity with no evidence of acute

AMI.

c. Ischemic stroke was defined as an acute episode of neurologic dys-

function caused by vascular injury lasting 24 hours or more.

d. A transient ischemic attack was defined as sudden onset of neurologic

symptoms, presumed to be ischemic, resolving in less than 24 hours,

clearly attributable to focal involvement of the central nervous system

(or of the eye) with no signs of a corresponding recent cerebral infarc-

tion on brain imaging.

e. Hemorrhagic stroke was defined as an acute episode of focal neuro-

logic symptoms with the presence of cerebral hemorrhage in the ap-

propriate territory on brain imaging (computed tomography or mag-

netic resonance imaging) caused by a nontraumatic intraparenchymal,

intraventricular, or subarachnoid hemorrhage.

f. PAD was defined as limb ischemia with major amputation or urgent

peripheral revascularization for ischemia.

30

Statistical analysis

In Study I, logistic regression was first used to analyze all associations of

klotho gene SNPs with mortality outcomes in an initial filtering process. Sub-

sequently, Cox regression was fitted to SNPs associated with a p value below

the 0.05 threshold, representing final results for those SNPs.53 The associa-

tions between genotypes for the SNPs and all-cause or CV mortality during

follow-up were evaluated using odds ratios and 95% confidence intervals

(CIs) with the homozygous major allele as reference in additive models. At

baseline, the models were tested unadjusted and adjusted for age, body mass

index, smoking, comorbidities (hypertension, diabetes mellitus, CAD, stroke,

cancer), eGFR, phosphate, and FGF23. Dominant and recessive genetics mod-

els were also analyzed. Cox regression models with unadjusted hazard ratios

(HRs) and 95% CIs were used to confirm findings. Tests for associations and

estimates for HRs were computed with adjustment for age, body mass index,

and smoking. Because eGFR and FGF23 were strongly correlated with mor-

tality, subgroup analyses on selected SNPs for mortality stratified by eGFR

(≥60 mL/min/1.73 m2, <60 mL/min/1.73 m2) and FGF23 (≥60 pg/mL,

<60 pg/mL). Kaplan–Meier curves with log-rank tests were used to examine

survival by klotho variant.

In Study II, cut-off values for analyses of mineral bone factors were based on

a literature review of associations of those factors with all-cause or CV mor-

tality. The selected cut-off values were 60 pg/mL for FGF23,54 60 ml/min for

eGFR,55, 56 50 nmol/l for vitamin D,57, 58 and 4.5 pmol/l for PTH.59-61 Kaplan–

Meier curves were generated to show the cumulative probabilities for all-

cause and CV mortality over 6 years’ observation time, and differences were

evaluated using a log-rank test. In addition, unadjusted associations of contin-

uous FGF23, PTH, eGFR, and vitamin D levels with all outcomes were as-

sessed and presented as restricted cubic splines with 3 knots to allow for non-

linear associations. Given that circulating FGF23 was highly correlated with

renal function, restricted cubic splines were also applied to demonstrate

FGF23-related outcomes associations stratified by renal function (normal re-

nal function, CKD stage 3A, and CKD stage 3B or less). Because of potential

nonlinear effects and interactions between mineral metabolic factors, the Ran-

dom Forest (RF) method was used to identify and rank relationships between

the predictive factors and the outcomes.62 The Gini index was applied to meas-

ure mineral metabolic factors and renal function importance in the RF algo-

rithm.63 The more important predictive factors had larger Gini scores.

To evaluate the interplay of bone and mineral metabolic factors with aspects

of kidney function related to the risk of all-cause and CV mortality, partici-

pants were allocated to these 3 groups: (1) Participants with normal eGFR (≧

31

60 ml/min), a normal vitamin D level, and FGF23 below 60 pg/ml. (2) Partic-

ipants with any single abnormality in these parameters. (3) Participants with

any 2 abnormalities in these parameters. After ensuring fulfillment of the pro-

portional hazards assumption by Schoenfeld residuals trend tests, multivaria-

ble Cox proportional hazards regressions were performed to find any associa-

tions of all-cause and CV mortality with mineral metabolic factors and abnor-

mal renal function components, with adjustment for age, baseline characteris-

tics (body mass index, smoking), and comorbidities (diabetes mellitus,

hypertension, stroke, and CAD). To determine the independent effect of

FGF23 and vitamin D on all-cause and CV mortality, adjusted analyses of the

associations were performed stepwise using Cox regression models with ad-

justment for factors including age, body mass index, smoking, comorbidities,

eGFR, and clinical laboratory data.

In Study III, the bone-associated proteins were elaborated using the RF

method in a multistage decision process that identifies and ranks relationships

between predictive and response variables. The RF algorithm constructs a

classification tree, randomly sampling the predictors, choosing the best split-

ting variables, and predicting new data by combining the predictions from all

trees to estimate the error rate and, in this case, to list the bone-associated

proteins that provide the highest predictive performance for all-cause and CV

mortality, and CVEs. The RF process62 can detect both linear and nonlinear

effects and potential protein-protein interactions, thereby identifying the pro-

tein that best differentiates groups. The Gini index was then applied to meas-

ure the importance of the bone-associated protein.63 The larger the Gini index

(range: 1–100), the more important the variable. Multivariable Cox regression

models were also used to determine the relationships between bone-associated

proteins (as continuous variables) and clinical outcomes of interest, adjusting

for confounders. All-cause mortality and CVE rates were displayed as

Kaplan–Meier curves, with the bone-associated proteins divided into tertiles.

Outcomes related to bone-associated proteins in their tertile groups and as

continuous variables were evaluated in 4 different models, with adjustment

for several confounders (Model 1: adjusted for age and sex; Model 2:

Model 1, plus blood pressure and comorbidities; Model 3: Model 2 plus la-

boratory data [albumin, CRP, low-density and high-density lipoprotein cho-

lesterol, phosphate] and dialysis duration; Model 4: Model 3 plus calcium and

PTH). The associations of the bone-associated proteins (as continuous varia-

bles) with the outcomes were further adjusted for single cytokine inflamma-

tory biomarkers (interleukins IL-6, IL-8, IL-16, IL-18, IL-27, IL-1RA, and

IL6-RA) separately.

To assess each biomarker’s incremental benefit, the C statistic was compared

using models with traditional risk factors with and without the addition of the

32

biomarker. The integrated discrimination improvement (IDI) and the continu-

ous (category free) net reclassification improvement (NRI) 64, 65 were esti-

mated to quantify the degree of correct reclassification as a result of adding

biomarkers to the clinical information risk model and the AURORA (A Study

to Evaluate the Use of Rosuvastatin in Subjects on Regular Hemodialysis: An

Assessment of Survival and Cardiovascular Events) risk model 66. The AU-

RORA risk prediction model contains age, albumin, CRP, history of CV dis-

ease, and diabetes.66

In study IV, univariable and multivariable Cox regression analyses were per-

formed to examine the risk for the clinical outcomes (all-cause and CV mor-

tality, and CVEs) in relation to PEA-selected CV protein biomarkers. That

analysis used continuous models (HR per standard deviation increase in nor-

malized protein expression). Multivariable Cox proportional hazards models

were adjusted for age, sex, smoking status, cause of ESKD, dialysis vintage,

dialysis treatment time per week, diabetes mellitus, previous myocardial in-

farction, previous unstable angina, previous cerebrovascular disease, previous

PAD, and blood levels of albumin, phosphate, and CRP.

33

Main results

Study I

In study I, polymorphisms in the klotho gene were analyzed using computa-

tional tools to predict coding and non-coding SNPs. SNPs (rs9536314,

rs9527025, rs9527026, rs564481) from the coding region (exon) were selected

for further bioinformatic analysis using SIFT, PolyPhen-2, PROVEAN,

SNPs3D, LS-SNPs, and MutPred. SNP rs9536314 was predicted to be “dam-

aging” in SIFT, “probably damaging” in PolyPhen-2, “deleterious” in

PROVEAN, and “deleterious” in SNPs3D and MutPred. SNP rs9527025 was

predicted to be “damaging” in SNPs3D, LS-SNPs, and MutPred.

In the additive univariate logistic regression model, 3 SNPs were associated

with increased risk of all-cause mortality, including the TT genotype of

rs9536282, the GG genotype of rs9536314, and the CC genotype of

rs9527025. However, none of the 18 SNPs were statistically significantly as-

sociated with the risk for CV mortality. In adjusted analyses controlling for

age, body mass index, smoking, comorbidities (hypertension, diabetes melli-

tus, CAD, stroke, cancer), eGFR, phosphate, and FGF23, the rs9536282,

rs9536314, and rs9527025 genotypes were not associated with all-cause or

CV mortality. In the analyses of dominant and recessive models, an associa-

tion of rs9527025 and rs9527026 with all-cause mortality was observed under

the recessive model. A Cox proportional hazards analysis to determine the

relationships of the rs9536314 and rs9527025 genotypes with mortality found

no statistical significance in either the unadjusted or adjusted model. Subgroup

analyses of SNPs rs9536314 and rs9527025 in the additive univariate Cox

regression models demonstrated no significant risk for all-cause or CV mor-

tality when stratified by eGFR or FGF23.

Study II

In study II, Kaplan–Meier curves showed greater all-cause mortality associ-

ated with higher FGF23 (≥60 pg/mL vs. <60 pg/mL), renal function impair-

ment (eGFR <60 mL/min vs. ≥60 mL/min), and vitamin deficiency (vita-

min D <50 nmol/L vs. ≥50 nmol/L)vitamin D. However, no difference in all-

cause mortality was found between the PTH groups (<4.5 pmol/L vs.

34

≥4.5 pmol/L). Similarly, in the survival analysis, participants having higher

FGF23 levels or renal function impairment compared with those having lower

FGF23 levels or normal renal function were also observed to have a higher

CV mortality rate. In the vitamin D and PTH subgroups, CV mortality was

not different.

Evaluating the nonlinear associations of mineral metabolic factors and renal

function with outcomes, the cubic spline demonstrated an increased risk for

all-cause mortality related to FGF23 level, renal function, and vitamin D level.

However, spline curves were found to be flat for the association of PTH level

with all-cause mortality and for that of vitamin D level with CV mortality.

Given that FGF23 was strongly correlated with renal function, the association

of all-cause mortality with FGF23 was stratified by renal function (normal

renal function, CKD stage 3A, and CKD stage 3B or lower). An increased HR

for all-cause and CV mortality was observed for an increased level of FGF23

in CKD stage 3B or lower. In addition, the Gini index was applied to the RF

analysis to compare the importance of FGF23, PTH, vitamin D, and renal

function. The highest-ranking factor for both all-cause and CV mortality was

FGF23.

Given the complex interaction between FGF23, PTH, vitamin D, and renal

function, we evaluated the associations between those factors in composite

and mortality risk. However, because the survival difference at the PTH cutoff

value was found to be insignificant, only the abnormal components of FGF23,

vitamin D, and renal function were evaluated in the composite analysis. Com-

pared with participants having a normal renal function (eGFR > 60 mL/min),

a normal vitamin D level, and low FGF23 (<60 pg/mL), those having renal

impairment (eGFR < 60 mL/min), vitamin D deficiency (<50 nmol/L), and

high FGF23 (≥60 pg/mL) had an increased risk for all-cause and CV mortal-

ity, after adjustment for confounders.

To evaluate the influence of confounding factors on FGF23 and vitamin D,

Cox regression models were used to investigate the associations of FGF23 or

vitamin D with outcomes, using stepwise adjustment for various factors. In

the unadjusted analyses, FGF23 and vitamin D were both associated with an

increased risk of all-cause mortality. However, the increased risk of all-cause

mortality in the high FGF23 group was attenuated after controlling eGFR.

Vitamin D deficiency was independently associated with all-cause mortality

even after controlling for all confounders. In contrast, vitamin D deficiency

was not associated with CV mortality. The increased risk of CV mortality in

the high FGF23 group was attenuated after controlling for other comorbidities

(diabetes, hypertension, stroke, and CAD). Longer follow-up on the clinical

outcome will be analyzed prior to the final sub-mission of this paper.

35

Study III

An importance plot of the relationships of bone-associated proteins with all-

cause mortality, CV mortality, and CVEs was produced by applying the Gini

index to RF analyses. The highest-ranking bone markers were TRAIL-R2 for

all-cause death, OPG for CV death, and OPG and TRAIL-R2 for CVEs. In

multivariable Cox regression analysis, only CTSL1 (HR: 1.62; 95% CI: 1.17

to 2.23) was associated with an increased risk for all-cause death. Four indi-

vidual bone markers were positively associated with CVEs: OPG (HR: 2.16;

95% CI: 1.44 to 3.26), TRAIL-R2 (HR: 1.66; 95% CI: 1.22 to 2.25), CTSD

(HR: 1.88; 95% CI: 1.24 to 2.85), and CTSL1 (HR: 1.63; 95% CI: 1.14 to

2.34).

The Kaplan-Meier plots of the associations between the bone marker tertiles

and the risk of all-cause death show a graded increase in risk in the higher

tertile groups of CTSL1, TRAIL-R2, and OPG. Higher concentrations of

CTSD, OPG, TRAIL-R2, and lower concentrations of RANKL were associ-

ated with increased risk for CVEs. Results for the relationships between the

bone markers and outcomes (all-cause death and CVEs) were similar in the

crude and adjusted analyses whether the tertile subgroup or continuous ap-

proach was used. After multivariable adjustment for age, sex, blood pressure,

comorbidities, laboratory data, HD treatment duration, and calcium and PTH

levels, the level of CTSL-1 was positively associated with an increased risk of

all-cause death. In the adjusted model for CVEs, 3 individual bone markers

(CTSD, OPG, TRAIL-R2) were associated with an increased risk for CVEs.

Because bone markers are moderately to strongly correlated with inflamma-

tory cytokines and considered to be potential confounders, the associations of

bone markers with outcomes (all-cause death and CVEs) were further adjusted

for cytokines individually. The association between all-cause mortality and

CTSL1 remained significant after adjustment for IL-6, IL-16, IL-18, IL-27,

IL-1RA, and IL6-RA. However, adjustment for IL-8 attenuated the associa-

tion between CTSL1 and risk for all-cause mortality (HR: 1.43; 95% CI: 0.98

to 2.08; p = 0.067). Adjustment for IL-8 also attenuated the associations with

CVEs of TRAIL-R2 (HR: 1.37; 95% CI: 0.96 to 1.96; p = 0.083), CTSD (HR:

1.38; 95% CI: 0.84 to 2.27; p = 0.2), and CTSL1 (HR: 1.25; 95% CI: 0.83 to

1.90; p = 0.29). Interestingly, the risk for CVEs of OPG remained significant

after adjustment for the various individual cytokines. For each unit increase in

OPG, the HR for CVEs after adjustment for IL-8 was 1.71 (95% CI: 1.06 to

2.78; p = 0.029).

OPG was the only bone-associated biomarker that improved the predictive

performance of our clinical risk model and the AURORA risk model. The

addition of OPG to a clinical risk model predicting CVEs showed a significant

36

NRI (0.32; 95% CI: 0.04 to 0.44; p = 0.03) and IDI (0.06; 95% CI: 0.01 to

0.10; p = 0.01). Adding OPG to the AURORA risk prediction model also re-

sulted in a NRI (0.21; 95% CI: 0.02 to 0.36; p = 0.02) and an IDI (0.04; 95%

CI: 0.004 to 0.09; p < 0.001).

Study IV

In multivariable-adjusted Cox models in study IV, 15 proteins were associated

with increased risk for all-cause death; 5, with increased risk for CV death;

and 17, with increased risk for CVEs. The adjusted Cox models also found 5

proteins related to a decreased risk for all-cause death and 2 related to a de-

creased risk for CV death.

A Venn diagram summarizing the PEA proteins associated with outcomes in

the adjusted multivariable model found that IL-8, T cell immunoglobulin and

mucin domain 1 (TIM-1), and C-C motif chemokine 20 (CCL20) were bi-

omarkers for all-cause and CV mortality, and CVEs. The HRs of IL-8 in the

adjusted multivariable model were 1.29 (95% CI: 1.11 to 1.51; p = 0.001) for

all-cause death, 1.34 (95% CI: 1.02 to 1.76; p = 0.036) for CV death, and 1.33

(95% CI: 1.11 to 1.59; p = 0.002) for CVEs. The HRs of TIM-1 in the adjusted

multivariable model were 1.26 (95% CI: 1.01 to 1.57; p = 0.036) for all-cause

death, 1.68 (95% CI: 1.15 to 2.45; p = 0.007) for CV death, and 1.34 (95% CI:

1.05 to 1.72; p = 0.017) for CVEs. The HRs of CCL20 in the adjusted multi-

variable model were 1.30 (95% CI: 1.11 to 1.52; p = 0.001) for all-cause death,

1.39 (95% CI: 1.08 to 1.79; p = 0.010) for CV death, and 1.27 (95% CI: 1.08

to 1.51; p = 0.005) for CVEs. Interestingly, the stem cell factor (SCF) and

galanin peptides (GAL) were both associated with reduced risk of all-cause

and CV mortality. The corresponding HRs in the adjusted multivariable mod-

els were, for SCF, 0.82 for all-cause death (95% CI: 0.69 to 0.96; p = 0.016)

and 0.72 for CV death (95% CI: 0.56 to 0.94; p = 0.015), and for GAL, 0.75

for all-cause death (95% CI: 0.63 to 0.90; p = 0.002) and 0.72 for CV death

(95% CI: 0.53 to 0.97; p = 0.033).

37

Additional Preliminary Study

Owning to another prospective hemodialysis cohort was conducted in Taiwan

Kaohsiung Medical University Hospital (KMUH), we launched a new study

to compare the hemodialysis patients between Denmark and Taiwan using in-

dividual data. The aim of the additional preliminary study was to compare

mortality and CVE differences for patients receiving HD in cohorts from Den-

mark (Århus HD cohort) and Taiwan (KMUH HD cohort). (Figure 3).

Figure 3. The study was designed to compare clinical outcomes in hemodialysis patients among Demark Århus cohort and the Taiwan KMUH cohort

Method and Statistical analysis of the additional preliminary study

Participants from the dialysis units of Kaohsiung Medical University Hospital

(KMUH) and Kaohsiung Municipal Hsiao-Kang Hospital were enrolled be-

tween August 2016 and January 2017. The enrolled participants were more

than 18 years of age and had received HD for more than 90 days. All partici-

pants received high-efficiency dialyzers therapy 3 times per week, with a tar-

get for adequate dialysis of Kt/V > 1.2. The HD parameters were 250–

300 mL/min blood flow, 500 mL/min dialysate flow, and 3.5–4 hours per di-

alysis session. The baseline characteristics of the patients (age, sex, dialysis

38

vintage, arteriovenous access type (fistula or graft), primary cause of kidney

failure [hypertension, diabetes, glomerulonephritis, or others], comorbidities,

medications, and biochemical data) were obtained from electronic health care

record systems. Hypertension was defined as blood pressure exceeding

140/90 mmHg or the use of blood-pressure-lowering drugs. Diabetes mellitus

was defined as HbA1C exceeding 6.5% or the use of glucose-lowering drugs.

The outcomes and mortality assessment were assessed as the previous defini-

tion (study III and study IV) by physician diagnosis in Demark Århus cohort

and Taiwan KMUH cohort. Kaplan–Meier curves were generated to show the

cumulative probabilities of all-cause mortality and CVEs over 4 years of ob-

servation time, and differences were examined with a log-rank test. Further,

the differences between the two regions in all-cause mortality and CVEs were

analyzed in 4 different Cox regression models, with adjustment for confound-

ers. (Model 1: age, sex, body mass index, systolic and diastolic blood pressure,

cause of ESKD, and HD vintage; Model 2: Model 1 plus comorbidities [dia-

betes mellitus, myocardial infarction, ischemic stroke]; Model 3: Model 2

plus clinical laboratory data [hemoglobin, albumin, low-density lipoprotein

cholesterol, CRP, total calcium, phosphate, PTH) and propensity score;

Model 4: weighted by stabilized inverse probability of treatment weighting).

The association between circulating high-sensitivity troponin T (hsTNT) and

outcomes (all-cause mortality and CVEs) in two cohorts was also evaluated

to illustrate the predictive effect across geographic regions.

Preliminary results of additional study

In the additional study, Demark’s Århus cohort and Taiwan KMUH cohort

were compared for differences in baseline characteristics, all-cause mortality,

and CVEs. Compared with patients in the KMUH cohort, patients in the Århus

cohort were older and more likely to be male. They also had a shorter HD

duration, a proportionally greater history of ischemic stroke, and a lesser his-

tory of diabetes mellitus. Patients in the Århus cohort also had a higher body

mass index; higher CRP, total calcium, phosphate, and hsTNT; and lower sys-

tolic and diastolic blood pressure, and PTH (Table 2).

Kaplan–Meier curves showed greater all-cause mortality and CVEs in the År-

hus cohort than in the KMUH cohort (Figure 4). After controlling for con-

founders, including in adjusted multivariable models and stabilized inverse

probability of treatment weighting models, Cox regression analysis found that

all-cause mortality and CVEs were greater in the Århus cohort than in the

KMUH cohort (Table 3).

39

Given that hsTNT was an excellent biomarker for predicting CVD, we further

evaluated its relationships with the high mortality risk cohort (Århus) and the

low mortality risk cohort (KMUH). Importantly, Kaplan–Meier curves for

hsTNT tertiles demonstrated that a higher level of hsTNT was associated with

higher risk for all-cause mortality and CVEs in patients at high CV risk (Århus

cohort) and in patients at low CV risk (KMUH cohort) (Figure 5 and 6).

Table 2. Baseline characteristics of participants in Taiwan KMUH hemodialysis co-hort and Demark Århus hemodialysis cohort

Taiwan KMUH

cohort

(N=347)

Demark Århus

cohort

(N=331)

P

Age, years 60.0 (51.0, 67.0) 67.1 (57.0, 76.5) <0.001

Male gender 187 (53.9%) 211 (63.7%) 0.011

Body mass index, kg/m2 23.6 (21.0, 25.8) 25.2 (22.2, 29.2) <0.001

Systolic blood pressure, mmHg 147.0 (128.5, 163.0) 141.0 (122, 158) 0.011

Diastolic blood pressure, mmHg 80.0 (70.0, 89.0) 69.5 (59.0, 79.0) <0.001

Cause of ESKD <0.001

Hypertension 37 (10.7%) 45 (13.6%)

Diabetes Mellitus 121 (34.9%) 61 (18.4%)

Glomerulonephritis 125 (36.0%) 39 (11.8%)

Others* 64 (18.4%) 186 (56.2%)

Hemodialysis vintage, years 5.0 (2.0, 12.0) 2.5 (0.9, 5.3) <0.001

Comorbidities

Diabetes mellitus 148 (42.7%) 90 (27.2%) <0.001

Myocardial infarction 59 (17.1%) 61 (18.4%) 0.713

Ischemic stroke 23 (6.6%) 61 (18.5%) <0.001

Clinical laboratory data

Hemoglobin, g/dL 10.7 ± 1.2 11.6 ± 1.3 <0.001

Albumin, g/dL 3.9 ± 0.3 3.9 ± 0.4 0.457

Low-density lipoprotein, mg/dl 93.0 (71.0, 116.5) 88.8 (65.6, 123.6) 0.816

C-reactive protein, mg/L 0.8 (0.2, 2.9) 5.7 (2.3, 15.0) <0.001

Total Calcium, mg/dL 9.3 ± 0.9 9.5 ± 0.8 <0.001

Phosphate, mg/dL 4.6 (3.9, 5.3) 6.3 (5.4, 7.4) <0.001

Parathyroid hormone, pg/ml 336.8 (149.6, 589.5) 198.1 (98.1, 333.0) <0.001

Cardiovascular biomarker

hsTNT, ng/l 52.0 (32.0, 81.0) 63.1 (40.5, 109.0) <0.001

Log-transformed hsTNT 1.7 ± 0.3 1.8 ± 0.3 <0.001

Abbreviation: ESKD, end-stage kidney disease; hsNTN, high sensitivity troponin T

Continuous variables as mean and standard deviation or median and interquartile

range; categorical variables as n (%).

*Other causes of end-stage renal disease include polycystic kidney disease, tumor,

systemic lupus erythematosus, gout, interstitial nephritis

40

Figure 4. Kaplan–Meier curves for (A) all-cause mortality and (B) composite vascu-lar events in patients of Demark Århus hemodialysis cohort and Taiwan KMUH he-

modialysis cohort

Figure 5. Kaplan-Meier curves of hsTNT tertiles for all-cause mortality in patients of Demark Århus hemodialysis cohort, Taiwan KMUH hemodialysis cohort, and in

both cohorts combined.

Figure 6. Kaplan-Meier curves of hsTNT tertiles for CVEs in patients

of Demark Århus hemodialysis cohort, Taiwan KMUH hemodialysis cohort, and in both cohorts combined

41

Table 3. The comparison of all-cause mortality, composite vascular events, and single vascular event (myocardial infarction, ischemic stroke, peripheral artery disease) in hemodialysis patients between Taiwan KMUH cohort and Demark Århus cohort

Outcomes Hazard Ratio (95% Confidence Interval)

Unadjusted Model 1a Model 2b Model 3c Model 4d

All-cause mortality

Taiwan KMUH cohort 0.22 (0.16 - 0.31)* 0.20 (0.13 - 0.31)* 0.20 (0.13 - 0.31)* 0.11 (0.02 - 0.54)* 0.14 (0.09 - 0.22)*

Demark Århus cohort 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]

CVE



Myocardial infarction

Taiwan KMUH cohort 1.18 (0.74 - 1.90) 0.89 (0.49 - 1.64) 0.87 (0.46 - 1.63) 0.76 (0.33 - 1.73) 1.23 (0.52 - 2.88)


Ischemic stroke

Taiwan KMUH cohort 0.45 (0.25 - 0.82)* 0.46 (0.20 - 1.04) 0.49 (0.21 - 1.11) 0.71 (0.24 - 2.11) 0.73 (0.26 - 2.07)


Peripheral artery disease


Demark Århus cohort 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference] aModel 1: adjusted for age, sex, body mass index, systolic blood pressure, diastolic blood pressure, cause of ESKD, and hemodialysis vintage. bModel 2: model 1 with further adjusted for comorbidities (Diabetes mellitus, Myocardial infarction, Ischemic stroke). cModel 3: model 2 with further adjusted for clinical laboratory data (Hemoglobin, Albumin, Low-density lipoprotein, C-reactive protein, Total

Calcium, Phosphate, Parathyroid hormone), and propensity score. dModel 4: Confounders control using weighted by stabilized inverse probability of treatment weighting (stabilized IPTW)

42

Discussion

Klotho gene SNPs and all-cause or CV mortality

In study I, SNPs in the klotho gene were evaluated by computational pro-

grams and databases that use various methods to predict SNP damage. The

nonsynonymous SNPs rs9536314 and rs9527025 were classified as the most

damaging. In the clinical study, unadjusted logistic regression analysis using

an additive model found an association of all-cause mortality risk with the TT

genotype of rs9536282, the GG genotype of rs9536314, and the CC genotype

of rs9527025. However, no statistically significant associations between pol-

ymorphisms in klotho and mortality risk were observed in unadjusted Cox

regression or adjusted logistic regression models. Results from the dominant

and recessive models were similar. Subgroup analyses of rs9536314 and

rs9527025 stratified by eGFR or FGF23 also produced negative results for the

MrOS cohort.

A nonsignificant trend of association was observed for the nonsynonymous

SNPs rs9536314 and rs9527025 with all-cause mortality, but not CV mortal-

ity. The SNPs rs9536314 (F352V) and rs9527025 (C370S) have been reported

to be related to CV diseases.67, 68 However, a meta-analysis of rs9536314

(F352V) and rs9527025 (C370S) with respect to CV disease failed to indicate

any statistically significant association in the additive genetic model, the dom-

inant genetic model, or the recessive genetic model.69 The minor allele fre-

quency differences for rs9536314 and rs9527025 in Sweden’s MrOs data and

other databases (the 1000 Genomes EUR Population, the HapMap CEU Pop-

ulation, and the Genome Aggregation Database European Population) might

partly explain the outcomes difference. Although computational prediction

signaled a “damaging” effect of the klotho KL-VS genotype in our study, no

statistically significant differences were observed in the cohort, possibly be-

cause the cohorts were different in age, sex, ethnicity, and comorbidities. The

functional klotho KL-VS variant results in amino acid substitutions that alter

secretion, catalytic activity, and functionality of the αkl protein.70, 71 However,

a study has also reported that substitution SNPs rs9536314 (F352V) and

rs9527025 (C370S) alter amino acid-related shedding and trafficking. Still,

the effects are small, and intragenic complementation could further minimize

the effects in vivo.72

43

Another klotho gene synonymous variant, rs564481 (C1818T), located in the

fourth exon, showed no association with all-cause or CV mortality in our

study, according to earlier studies that showed no CVD correlation for this

polymorphism.68, 69, 73 Although rs564481 is a synonymous variant, it is likely

to be functionally relevant, given that reports have associated it with CV risk

factors (blood pressure, glucose metabolism, and lipid levels)74, 75 and CAD76

in Asian populations. The SNP rs577912 was previously related to higher

mortality risk in patients receiving hemodialysis77 but our clinical study re-

sulted in negative findings. Interestingly, the computational tool FATHMM

predicts that the rs577912 genotype could be a damaging intron non-coding

region; however, no association with mortality outcomes was found in our

study.

The mineral bone factors and all-cause or CV mortality

In Study II, we demonstrated that high FGF23 was associated with all-cause

and CV mortality, especially in individuals with moderate to severe CKD

(CKD 3B or worse). Moreoever, in an RF analysis, compared with vitamin D

or PTH, FGF23 was found to be the mineral metabolic factor most important

to all-cause mortality and CV mortality. However, that association was atten-

uated after controlling for renal function. Conversely, vitamin D deficiency

was independently associated with all-cause mortality, but not CV mortality.

High and low PTH showed no relationship with mortality in our study. In a

combined evaluation, as the values for FGF23, vitamin D, and renal function

became more abnormal, all-cause and CV mortality were found to rise. Be-

cause compensation effects between these mineral bone factors are known,

FGF23 excess, vitamin D deficiency, and renal impairment together imply

loss of the compensating effect and might predict poor outcomes. Our findings

tease out the complexity of these mineral metabolic factors, renal function,

and mortality relationships.

In our study, the association of FGF23, but not vitamin D, with the risk for

all-cause mortality attenuated after adjustment for eGFR, indicating that renal

function remains the central factor. In contrast, the association of vitamin D

deficiency with mortality risk was independent of known mineral metabolism

and renal function. As reported, vitamin D deficiency was found in several

meta-analytic studies to be associated with mortality.78-80 However, vitamin D

deficiency can result from comorbidities and nutrient deficiencies, which

could themselves explain the observed links with mortality.81, 82 Currently, no

conclusive evidence shows that vitamin D supplementation provides a sur-

vival benefit in adults.83 In fact, vitamin D supplementation increases FGF23

concentrations84, which might counter the benefits of vitamin D supplements.

44

FGF23 can suppress the production of active vitamin D metabolites. Any det-

rimental effect of FGF23 might be influenced by reduced vitamin D activity.85

In our study, vitamin D deficiency was found to be a factor independently as-

sociated with all-cause mortality beyond FGF23. Other reports support our

finding that the highest risk for all-cause mortality was found in hemodialysis

patients with a low vitamin D level and with a high FGF23 level.19

High serum FGF23 has been observed as a physiologic reaction to impaired

renal function when renal phosphate excretion is compromised.86 The health

impact of these mineral bone factors should therefore be evaluated simultane-

ously. During the early stages of CKD, expression of the renal FGF23 co-

receptor, αkl, progressively declines in response to kidney damage and de-

clines along with the loss of functional nephrons are lost, promoting partial

resistance to FGF23’s physiologic actions.87-89 As a compensatory mecha-

nism, FGF23 rises above normal values by a factor of 1000 to maintain a neu-

tral phosphate distribution.87, 90, 91 The compensatory increase in FGF23 pro-

motes the suppression of 1-25-dihydroxyvitamin D production, which in turn

promotes the elevation of PTH, causing secondary hyperparathyroidism.92

Thus, renal dysfunction is the major contributing factor to increased circulat-

ing FGF23. As demonstrated in our study, high FGF23 was not associated

with risk for all-cause mortality after adjustment for eGFR. Thus, high FGF23

is a consequence, but not a cause, of risk for mortality.

FGF23 has been associated with non-CV causes of death,93 reflecting a lack

of specificity between a rise in FGF23 concentration and disease risk. In our

study, a higher level of FGF23 was not associated with increased CV mortality

after adjustment for diabetes, hypertension, stroke, and CAD. Theoretically,

FGF23 rises before any other marker of mineral bone disorder, and so it tem-

porally mirrors the rise in CV risk as kidney disease progresses. Many condi-

tions can cause that rise, and its presence and severity generally reflect disease

activity. Nevertheless, elevated FGF23 has still been associated with mortality

across a spectrum of CKD.91, 92 In our study, lower CKD status, particularly

eGFR less than 45 ml/min, produced higher mortality spline plots. Because

the kidney is the main source of systemic αKlotho, any reduction in nephron

numbers and in eGFR is followed by a progressive decline in circulating

αKlotho.94 That the protection conferred by αkl is greater than the deleterious

effect of FGF23 has been demonstrated in clinical95 and animal studies.96 This

raises the possibility that αKlotho deficiency may be the primary alteration in

MBD.97 Thus, we found that renal function is the crucial factor contributing

to the risk association between FGF23 and mortality.

45

Bone-associated markers and mortality or CVE in hemodialysis patients

In Study III, we evaluated the relationships of 9 bone-associated biomarkers

with all-cause and CV mortality, and CVEs in 331 patients receiving HD. Us-

ing an RF approach, the importance of the bone biomarkers with respect to

outcomes was evaluated, and the top biomarkers were TRAIL-R2 for all-cause

death, OPG for CV death, and both OPG and TRAIL-R2 for CVEs. However,

only OPG was independently associated with CVEs in a cytokine adjustment

model. OPG could potentially provide clinical information beyond that cur-

rently available for the prediction of CVEs. Relationships of the bone-associ-

ated proteins, such as OPG, TRAIL-R2, CTSD, and CTSL1, with CV disease

have previously been reported.98-105 Our study also showed a relationship be-

tween TRAIL-R2 and OPG106 with higher CV risk. However, in a departure

from previous reports,104, 107 we found no correlation between low TRAIL and

poor outcomes.

In our study, OPG was the strongest predictor of CVEs. Elevated OPG has

previously been associated with aortic stiffness and coronary artery calcifica-

tion,108, 109 as well as CV outcomes.110 Interestingly, the RF approach was re-

cently used to show an association of OPG with CVEs in non-dialysis CKD.

Forne et al. investigated 19 biomarkers in a subcohort of the NEFRONA study

(Observatorio Nacional de Atherosclerosis en NEFrologia).111 They used var-

iable importance from the RF analysis for CV risk in the first step and then

evaluated the most promising variables for CVE in a Fine and Gray competing

risks model. OPG was a potential predictor of CV risk in the RF analysis and

also a statistically significant predictor of increased CVEs in the competing

risks model.111 Elevated OPG had already been associated with increased CV

mortality112-114 in patients treated with hemodialysis. Compared with our

study, the study by Sigrist et al. enrolled patients receiving hemodialysis who

were younger and had lower mortality rates109 ; patients receiving hemodialy-

sis who were enrolled in other studies by Morena et al.112 and Winther et al.114

had a higher prevalence of baseline CV diseases. In contrast to these previous

studies,112-114 OPG was not an independent risk marker for all-cause mortality

in our study. The results of discrepancies may exist because of different con-

founders' adjustments across studies.

Although a positive association between baseline OPG concentration and risk

for CV disease has been shown, some analytic issues should be discussed. No

study has evaluated the independent association between OPG and CVEs in a

model adjusted for inflammatory cytokines.108, 109, 112, 113, 115 Inflammation is

well known to be a significant risk factor for mortality and CV events in pa-

tients with CKD.116 Inflammatory cytokines influence the production of OPG,

suggesting that the OPG/RANKL/RANK system plays a modulatory role in

46

vascular injury and inflammation.117 One study suggested that OPG levels are

significantly correlated with inflammatory markers, such as IL-6.118 As

demonstrated in our study, OPG was positively associated with inflammatory

cytokines, such as IL-6, IL-8, IL-16, IL-18, IL-27, IL-1RA, and IL-6RA.

However, we found no significant correlation between OPG and CRP, in line

with previous studies in patients with CKD and those receiving hemodialy-

sis.109, 112 It has been suggested that OPG alone or in combination with elevated

CRP is associated with worse outcomes in such patients.109, 112 In addition,

after adjustment for confounders (including CRP), OPG was found to be a

significant independent predictor of PAD in patients receiving peritoneal di-

alysis.119 In our adjusted multivariable model, we also demonstrated that OPG

predicts CVEs even after adjustment for CRP. Furthermore, we demonstrated

that the association between OPG and CVEs was independent of cytokine ac-

tivity or calcium/phosphate/PTH parameters. More studies are needed to con-

firm our findings.

CV-associated markers for mortality or CVE in hemodialysis patients

In Study IV, a recent PEA-based proteomics study in the Mapping of Inflam-

matory Markers in Chronic Kidney disease (MIMICK) cohort, TIM-1, matrix

metalloproteinase (MMP)-7, tumor necrosis factor TNF receptor 2, IL-6,

MMP-1, brain natriuretic peptide, ST2, hepatocyte growth factor (HGF),

TRAIL-R2, spondin-1, and FGF23 showed significant associations with CV

death after adjustment for age and sex in 183 patients receiving HD.120 Several

of the proteins reported in the MIMICK study were also correlated with CVEs

in our study—specifically, TIM-1, MMP-7, IL-6, brain natriuretic peptide,

HGF, and TRAIL-R2. However, some proteins associated with CV death in

our study (e.g., tissue plasminogen activator [tPA], CCL20, GAL, IL-8, and

SCF) were not associated with CV death in the MIMICK cohort. Importantly,

TIM-1, also known as kidney injury molecule-1, was the most important risk

marker for CV death in the MIMICK cohort,120 a result confirmed in our study.

Importantly, TIM-1, also known as kidney injury molecule-1, was the most

important risk marker for CV death in the MIMICK cohort.120 There could be

several reasons for the differences in the two studies, including a smaller sam-

ple size in MIMICK (n=183), the longer dialysis vintage in our study, and

adjustment for different confounders. In addition, the definition of CV death

was validated by a physician in our study, while the MIMICK cohort used

International Classification of Diseases diagnostic codes. Moreover, in a dis-

covery-validation study of individuals with CKD not receiving dialysis,

MMP-12 was significantly associated with an increased risk of major adverse

CV events (fatal or nonfatal myocardial infarction or fatal and nonfatal

47

stroke).121 That research also agrees with our finding that MMP-12 is an inde-

pendent biomarker predicting CVEs in patients receiving HD.

Chronic inflammation is an essential CV risk factor in patients with kidney

disease, characterized by enhanced production of CRP and other inflamma-

tory mediators, including IL-6 and IL-8. Those cytokines and chemokines

were previously associated with all-cause and CV mortality in patients receiv-

ing hemodialysis.122-125 In line with previous studies in such patients,126 EN-

RAGE, another inflammatory biomarker, was also positively associated with

CVEs. Furthermore, we found that TRAIL-R2 (member protein of the TNF-

receptor superfamily) and OPG, reflecting the bone-vascular axis, were asso-

ciated with a higher risk of CVEs. OPG has previously been described as a

CV marker for all-cause death in hemodialysis patients,112-114 and TRAIL-R2

is present in human atherosclerosis lesions, with higher expression in vulner-

able plaques than in stable ones.106

Potential mechanisms and clinical studies of novel biomarkers

Several novel biomarkers, such as HGF, CCL20, tPA, GAL, and SCF, were

related to death or CV outcomes.

HGF is a pleiotropic cytokine involved in regulating several biologic pro-

cesses, such as cardiometabolic activity, inflammation, angiogenesis, and tis-

sue repair.127 HGF is associated with CAD128 and ischemic stroke129 in patients

without CKD. In hemodialysis patients, higher levels of HGF are associated

with concentric left ventricular geometry130 and cerebral infarction.131 Those

associations might be related to leukocyte activation during hemodialysis.132

Our study is the first to demonstrate that HGF is associated with an increased

risk of both all-cause mortality and CVEs in hemodialysis patients.

Previous studies have shown that CCL20 excretion is increased in patients

with stage 5 CKD compared with healthy individuals and patients with

stages 1–3 CKD.133 Furthermore, patients with ischemic heart disease have

higher levels of circulating CCL20,134 and CCL20 is expressed in atheroscle-

rotic plaques.135 A recent study demonstrated that CCL20 is associated with

increased CVEs in patients with stage 3-5 CKD.121 CCL20 contributes to vas-

cular endothelial inflammation136 and triggers pathways similar to those acti-

vated by low-density lipoprotein cholesterol.135 The IL-6, IL-8, and CCL20

cytokines or chemokines have been associated with the Th17 CD4 lympho-

cyte.137 Several studies have shown that Th17 cells play a critical role in arte-

riosclerosis development.138 Genetic deletion of CCL20 receptors in Apoe−/−

mice decreases atherogenesis and endothelial inflammation.139 We, therefore,

speculate that the accumulation of CCL20 might significantly affect the risk

for CV disease in patients receiving hemodialysis.

48

The tPA glycoprotein is involved in coronary plaque rupture140 and is consid-

ered a marker for regulating endogenous fibrinolysis.141 In the general popu-

lation without kidney disease, there is an association between elevated circu-

lating levels of tPA and subsequent coronary heart disease.141 In general, cir-

culating levels of tPA are elevated in patients receiving dialysis compared

with healthy individuals,142 and circulating levels of tPA in hemodialysis pa-

tients are positively associated with several CV risk factors, such as age,

smoking, blood pressure, and CRP.142

GAL is an endocrine hormone of the central and peripheral nervous systems

involved in central CV regulation, affecting heart rate and blood pressure.143

However, functional properties of the galaninergic system are not fully eluci-

dated for cardiac diseases. In an animal model, GAL can limit myocardial in-

farction size and improve post-ischemic cardiac function recovery.144 It also

suppresses myocardial apoptosis and mitochondrial oxidative stress in cardiac

hypertrophic remodeling.145 These basic studies could support our results of

the negative association between GAL and CV mortality in patients receiving

hemodialysis.

The association between high levels of SCF and lower all-cause or CV mor-

tality was a novel finding of our study. Similar results were described in the

general population (Malmö Diet and Cancer study)146 and in patients with sta-

ble CAD.147 SCF is involved in vasculogenesis and cardiac repair by stimulat-

ing the recruitment and activation of bone marrow-derived stem cells and tis-

sue-resident progenitors.148 An increase in SCF expression occurs naturally in

response to myocardial infarction, which mediates the migration of c-kit+ car-

diac and bone marrow cells to the injured area for cardiac remodeling.149 We

found that circulating SCF was positively correlated with albumin. Albumin

is well known to be a predictive marker in patients receiving hemodialysis.

We speculate that SCF might play a protective role in vascular injury and

could reflect the clinical nutrition status in patients receiving hemodialysis.

Mortality differences among hemodialysis patients between Asian and Caucasian

In the additional preliminary study, we found that patients in Demark’s År-

hus HD cohort had a higher risk for all-cause mortality and CVEs than patients

in Taiwan’s KMUH HD cohort. Interestingly, racial and ethnic differences in

mortality for patients receiving HD have been reported in several studies.150-

153 Some reports suggested a 25% to 35% lower mortality rate in Asian pa-

tients than in White patients.154, 155 The causes of these ethnic disparities re-

main largely unknown. Factors that might explain the mortality differences

49

include genetic and environmental factor differences151 and racial disparities

in diet, nutrition, and inflammation.156 This recent comparison between hemo-

dialysis patients in the two nations will be developed further into a full manu-

script and hopefully published in the near future.

50

Strengths and limitations

Strengths of Studies I and II include the large sample size of the MrOS cohort

of community-dwelling men with nearly complete follow-up of surviving co-

hort participants, and outcome measures that were well-validated because of

high-quality national registers. We used 14 different computational tools in a

bioinformatics approach, plus a clinical study, to perform a complete evalua-

tion of klotho gene polymorphism. However, several limitations must still be

addressed. First, self-reported questionnaires were used at baseline visits, and

we cannot exclude the possibility of underestimation of smoking or disease

prevalence. Second, the study was observational, and so causality was not pos-

sible to cannot be determined. Third, the generalizability of our study is lim-

ited to healthy community-dwelling Swedish males. Fourth, the minor allele

frequency of rs9536314 homozygotes could be different in elderly and young

subjects.70 The latter limitation might also affect our study result because of

the enrolled elderly population in the MrOS cohort (Study I). Fifth, FGF23,

vitamin D, and PTH were measured at a single point, so we could not evaluate

those mineral metabolic factors as time-varying covariates (Study II). Sixth,

residual and unmeasured confounders might remain despite the attempt to ad-

just for potential confounders such as genetics and other CV biomarkers

(hsTNT) (Study II). Lastly, we could not evaluate other FGF23 regulators or

mediators (i.e., iron metabolism,157, 158 inflammatory proteins,158, 159 or 1-25-

dihydroxyvitamin D158) because of unavailable data in the Sweden MrOs co-

hort (Study II).

In Studies III and IV, the strengths of our investigation include a longitudinal

study design, proteomics chip for simultaneous measurement of multiple

bone-associated proteins, and exploration analysis using the RF approach.

Limitations include the observational design, including prevalent patients with

varying disease durations and low statistical power in some of our analyses.

The study focused mainly on White patients receiving HD therapy in Scandi-

navia, so extrapolations to non-White patients receiving HD or patients with

CKD not yet on dialysis should be made cautiously. Furthermore, the scale

for PEA-based protein levels is not an absolute concentration, so the perfor-

mance of the protein biomarkers and definition of risk thresholds in a clinical

setting warrants further study or the use of methods yielding absolute values,

possibly by calibrating the PEA values. The study also used single assess-

ments of the proteomic assay, so misclassification and short-term variability

are potential issues. Finally, the number of statistical tests performed for this

manuscript increases the risk of spurious findings; hence, novel findings not

previously published have to be replicated in an independent cohort of pa-

tients.

51

Conclusion and future perspectives

This thesis contributes to the identification of biomarkers related to all-cause

mortality, CV mortality, and CVEs in patients in general and in those with

ESKD, but especially markers targeting the bone–vascular axis. In Sweden’s

MrOs elderly community cohort, two potentially damaging SNPs (rs9536314

and rs9527025) in the klotho gene were not associated with all-cause mortality

or CV mortality (Study I). However, composite evaluation of elevated

FGF23, vitamin D deficiency, and renal impairment was associated with all-

cause and CV mortality. Among the bone-related factors, elevated FGF23 was

the most important marker for predicting mortality, but it was clearly influ-

enced by renal function. Long-term follow-up data on the outcome will be

analyzed prior to submission of the study (Study II). In Denmark Århus co-

hort of individuals receiving hemodialysis, one of the most promising bone-

related proteins, OPG, was associated with CV events independent of cytokine

activity (Study III). Using the PEA proteomics approach, we identified

higher IL-8, TIM-1, and CCL20, as well as lower SCF and GAL, as being

associated with poor outcomes (Study IV). We addressed the issue of the

bone–vascular axis and CVD. We evaluated from gene levels to circulating

protein levels and from the general population to patients with CKD. Based

on our research findings, more evidence was generated to link bone and vas-

cular complications. We also identified several CV proteins considered to be

potential biomarkers in patients receiving HD. Because we had 2 independent

prospective HD cohorts (Demark Århus cohort and Taiwan KMUH cohort), a

direct comparison of mortality and CVEs between the two geographic regions

was intriguing. We found a higher risk for all-cause mortality and CVEs in the

patients of the Århus cohort than in the patients of the KMUH cohort. Inter-

estingly, hsTNT can adequately predict outcomes in both cohorts. This will

all be analyzed further and developed into a separate communication (Addi-

tional preliminary study).

For the future, a simultaneous evaluation of PEA-based CV proteomics in the

Århus and KMUH cohorts will be fascinating. Given the risk for death or

CVEs triggered by uremic toxins in patients with kidney disease, an investi-

gation of molecules associated or interacting with uremic toxins and CV pro-

teomics could illuminate the pathophysiology of uremic toxins with respect to

the CV system. Given that the gut microbiota produces many uremic toxins, a

study to link the gut microbiome, uremic toxins, CV protein biomarkers, and

52

clinical CV phenotype would be reasonable. A CV risk prediction model

based on different omics data could be constructed to improve risk stratifica-

tion in patients with kidney disease. Based on the principles of precision med-

icine, we expect an early diagnosis and early treatment to overcome CVD in

patients with CKD.

53

Summary in Swedish (sammanfattning på svenska)

Denna avhandling bidrar till att identifiera biomarkörer som är relaterade till

allmän dödlighet, kardiovaskulär (CV) dödlighet, samt kardiovaskulära hän-

delser hos patienter med eller utan njursvikt. Ett särskilt fokus har varit på

biomarkörer involverade i ben-kärl-axeln.

Bland äldre män i Sverige (Sweden MrOs kohort) var två potentiellt skadliga

SNPs (rs9536314 och rs9527025) i Klotho-genen inte associerade till allmän

eller specifik CV-dödlighet (studie I). En sammansatt bedömning av förhöjda

FGF23-nivåer, D-vitaminbrist och njurfunktionsnedsättning var dock associ-

erad med dödlighet i allmänhet och även specifikt CV-dödlighet. Bland ben-

relaterade faktorer var förhöjda nivåer av FGF23 den viktigaste markören för

att förutsäga mortalitet, men denna faktor var tydligt relaterad till njurfunkt-

ionsstatus (studie II). Hos hemodialys (HD) patienter i en Dansk kohort var

osteoprotegrin det benrelaterade protein som var starkast relaterat till CV-hän-

delser oberoende av annan cytokinaktivitet (studie III). Med hjälp av PEA-

baserad metod för proteomik kunde vi slå fast att högre nivåer av IL-8, TIM-

1 och CCL20, samt lägre nivåer av SCF och GAL, var förknippade med ökad

mortalitetsrisk och även risk för CV händelser (studie IV).

Fokus i studierna har varit på proteinmarkörer, speciellt inom ben-kärl-axeln,

och kardiovaskulär sjuklighet. Vi har utvärderat sambanden både på gennivå

och vad gäller cirkulerande proteinnivåer. Studierna utfördes både inom den

allmänna befolkningen och i patienter med njursjukdom. Baserat på dessa

forskningsresultat föreligger det ett tydligt samband mellan benomsättning

och vaskulära komplikationer. Forskningsprojektet har även identifierat ett

flertal proteiner som kan betraktas som potentiella biomarkörer för hjärtkärl-

sjukdom hos HD-patienter.

54

Acknowledgments

There are many I would like to thank. This thesis is the result of many collab-

orations and the support and effort of many people. I am most grateful to

Östen Ljunggren, my main supervisor, has introduced me to the concept of

mineral bone disease. I appreciate your positive attitude to help me with the

research work and allow me to take lots of different courses.

Bengt Fellström, my co-supervisor and the senior mentor, your engagement,

determination, and enthusiasm for science have been invaluable for my devel-

opment as a researcher. You have always been supportive when I struggled

and encountered difficulties. Thank you for sharing your experience with car-

diovascular disease research in nephrology. You allow me to join this fantastic

field.

Torbjörn Linde (my co-supervisor) and My Hanna Sofia Svensson (my co-

author for study III and study IV), your wise advice in scientific writing and

commentaries on our research works were inspiring.

Rie Io Glerup and Per-Anton Westerberg, your data sharing and detailed in-

troduction for the prospective cohort helped me a lot.

I want to thank all colleagues in Kaohsiung Medical University Hospital, Tai-

wan. Mei-Chuan Kuo, my main supervisor in Taiwan, I must express my grat-

itude to you for guiding me in the thinking process, clinical experience, and

knowledge of nephrology. Yi-Wen Chiu, you are brilliant, knowledgeable,

and the best leader to support my research work in Taiwan. I appreciate your

patience and guidance.

A special thanks to my family for supporting me to study abroad and helping

me to take care of our baby. It’s such a blessing that I have you in my life.

55

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Acta Universitatis Upsaliensis Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine 1785

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A doctoral dissertation from the Faculty of Medicine, Uppsala University, is usually a summary of a number of papers. A few copies of the complete dissertation are kept at major Swedish research libraries, while the summary alone is distributed internationally through the series Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine. (Prior to January, 2005, the series was published under the title “Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine”.)

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