Successes Achieved and Challenges Ahead in Translating Biomarkers
into Clinical Applications
Greg Tesch,1Shashi Amur,2John T. Schousboe,3,4Jeffrey N. Siegel,5Lawrence J. Lesko,6and Jane P. F. Bai6,7
Received 5 November 2009; accepted 28 January 2010; published online 16 March 2010
Abstract. Biomarkers are important tools for identifying and stratifying diseases, predicting their
progression and determining the effectiveness, safety, and doses of therapeutic interventions. This is
important for common chronic diseases such as diabetic nephropathy, osteoporosis, and rheumatoid
arthritis which affect large numbers of patients worldwide. This article summarizes the current knowledge
of established and novel biomarkers for each of these diseases as presented at the 2008 AAPS/ACCP
joint symposium “Success Achieved and Challenges Ahead in Translating Biomarkers into Clinical
Applications,” in Atlanta, Georgia. The advantages and disadvantages of various proteomic, metab-
olomic, genomic, and imaging biomarkers are discussed in relation to disease diagnosis and stratification,
prognosis, drug development, and potential clinical applications. The use of biomarkers as a means to
determine therapeutic interventions is also considered. In addition, we show that biomarkers may be
useful for adapting therapies for individual needs by allowing the selection of patients who are most
likely to respond or react adversely to a particular treatment. They may also be used to determine
whether the development of a novel therapy is worth pursuing by informing crucial go/no go decisions
around safety and efficacy. Indeed, regulatory bodies now suggest that effective integration of biomarkers
into clinical drug development programs is likely to promote the development of novel therapeutics and
more personalized medicine.
KEY WORDS: biomarkers; bone; diabetic nephropathy; drug development; genetic; inflammation;
osteoporosis; proteomics; rheumatoid arthritis.
Biomarkers are frequently used for disease diagnosis and
stratification, treatment selection, monitoring disease pro-
gression, and establishing the patients' responses to therapies
(efficacy or adverse events). For example, low-density lip-
oprotein cholesterol levels and blood pressure are recognized
surrogate endpoints in developing drugs for treating cardio-
vascular disease, fasting blood glucose and glycosylated
hemoglobin levels measure diabetes control, and plasma
thyroid levels are indicators of thyroid function. Recently,
scientific and technological advancements have seen an
exponential growth of biomarkers derived from proteomics,
metabonomics, and genomics. Efforts are being made to
dovetail these scientific branches of biology into an integrated
wealth of biomarker knowledge for providing the best
evidence-based health care. Proteomic and metabonomic
biomarkers, although diverse, are potentially related to the
clinical manifestation of disease and to patient's responses to
drug treatment. These categories of biomarkers have begun
to be applied in drug discovery and development. Genomic
biomarkers are adding another dimension to health care by
enabling us to understand the germ line and somatic basis of
disease development and of differential clinical responses to a
specific medical treatment.
Despite current therapies, chronic diseases such as
diabetic nephropathy, osteoporosis, and rheumatoid arthritis
remain incurable and, in some cases, not effectively treated.
The increasing size and longevity of our population makes
these diseases a major clinical problem and a growing
financial burden. The current review article summarizes the
current knowledge of established and novel biomarkers for
diseases presented at the 2008 AAPS/ACCP joint symposium
“Success Achieved and Challenges Ahead in Translating
Biomarkers into Clinical Applications,” in Atlanta, Georgia,
and highlights the challenges that researchers and clinicians
are facing in the development and application of biomarkers
to manage and treat these diseases.
1Department of Nephrology, Monash Medical Center, Clayton,
2Genomics group, OCP, OTS, CDER, FDA, Silver Spring, Maryland,
3Park Nicollet Health Services, Minneapolis, Minnesota, USA.
4Division of HealthPolicy and Management, Universityof Minnesota,
Minneapolis, Minnesota, USA.
5Division of Anesthesia, Analgesia and Rheumatology Products
(DAARP), OND, CDER, FDA, Silver Spring, Maryland, USA.
6OCP, OTS, CDER, FDA, Silver Spring, Maryland, USA.
7To whom correspondence should be addressed. (e-mail: jane.
The AAPS Journal, Vol. 12, No. 3, September 2010 (#2010)
1550-7416/10/0300-0243/0#2010 American Association of Pharmaceutical Scientists
INTEGRATION OF BIOMARKERS IN DRUG
AND CLINICAL PRACTICE
Use of biomarkers in drug development has been
identified as a key prospect in the US FDA's Critical Path
Initiative, to resolve the discrepancy between increasing
resources going towards development and the decrease in
the number of submissions for drug approval (1–3). Effective
integration of biomarkers into clinical drug development
programs is expected to promote the development of novel
therapeutics and personalized medicine.
genomic and pharmacogenetic biomarkers, has become avail-
able since last decade. A similar but slower trend is observed in
the incorporation of genomic/genetic information in regulatory
submissions. The genomics group in the Office of Clinical
Pharmacology/Center for Drug Evaluation and Research
(CDER) is leading CDER's efforts on incorporating genomics
into drug development and patient care. The core mission of
thegenomics groupis to reviewgenomic/genetic information in
regulatory submissions and to participate in guiding drug
developers in optimizing drug development.
In order to encourage submission of biomarker data in
regulatory submissions, a guidance document on pharmaco-
genomics data submissions was published by FDA in March
2005 (4). In addition, a voluntary genomic data submission
process has been set up at FDA to facilitate scientific
exchange including discussion on technologies, data analysis
methods, and genomic/genetic biomarkers, outside the regu-
latory framework. This program was later expanded to
voluntary exploratory data submissions (VXDSs) to include
other exploratory data such as proteomics and metabolomics
data. The VXDS program has received more than 50 VXDS
submissions and has had a significant impact on science and
on development of new policy (5). The VXDS program has
also been used strategically to discuss the possible use of
biomarkers in drug development studies. A pilot process for
qualification of biomarkers has also been set up to facilitate
translation of exploratory biomarkers to qualified biomarkers
(6). Recently, the US FDA and EMEA concluded that seven
kidney injury preclinical biomarkers submitted by Predictive
Safety Testing Consortium (PSTC) are considered qualified
biomarkers (7). These biomarkers can now be used in pre-
clinical safety studies to detect drug-induced nephrotoxicity.
Pharmacogenomic biomarkers can be applied in targeted
drug design to improve efficacy of drugs. The biomarkers can
also be applied to identification and exclusion of biomarker
positive/negative patients at risk of developing serious
adverse events. In addition, pharmacogenomic biomarkers
can be used in dose selection/adjustment of drugs to improve
efficacy and safety of drugs. The analytical performance
characteristics should be established before utilizing the
biomarker in stratification, identification of responders, or
as tests to avoid prescribing to either biomarker positive or
biomarker negative subjects.
Drug Efficacy/Safety and Biomarkers
Biomarkers can be used in designing drugs that selec-
tively target populations effectively and help select potential
responders. For example, maraviroc, an antiviral drug
approved for treatment of human immunodeficiency virus
(HIV) patients, is a targeted therapeutic. This drug was
designed to block the cellular CCR5 receptor and prevent
infection of the host cells by HIV-1. Thus, maraviroc is
effective in patients who harbor CCR5-tropic HIV-1 (8).
Another example is trastuzumab, a recombinant DNA-
derived humanized monoclonal antibody that selectively
binds to human epidermal growth factor receptor 2, HER2.
This biologic was developed to treat a subpopulation of
breast cancer patients (25–30% of primary breast cancers)
who overexpress HER2. Trastuzumab has been shown to
inhibit the proliferation of human tumor cells that over-
express HER2 in both in vitro assays and in animals before
demonstrating efficacy in clinical trials (9).
Dose adjustment. Interindividual variability makes it
challenging to find a dose of a drug that works for all
patients. One of the strategies used to circumvent the
situation is dose adjustment, increasing the dose if the efficacy
is low or decreasing the dose if adverse events occurred. As
the understanding of the association between polymorphisms
in genes of drug metabolizing enzymes (DME) or drug
transporter proteins (DTP) with systemic exposure (pharma-
cokinetics) and with adverse events increases, it may be
possible to have a dosing strategy based on the genetic
polymorphisms of DMEs and DTPs. Some of the drug labels
have already been updated with new information. For
example, azathioprine label was updated to recommend
genotype or phenotype patients for thiopurine methyltrans-
ferase (TPMT) (10). This recommendation is based on the
results of studies that patients with TPMT deficiency or with
lower activity are at increased risk for myelotoxicity, and the
absence or decrease in TPMTactivity was caused by mutation
and, thus, could be predicted by genotyping data.
Exclusion of patients at risk of developing adverse
reactions. This strategy is applicable only when patients at
risk can be identified before prescribing the drug. One
example is HLA-B*5701 genotyping to identify HIV patients
at risk of developing abacavir hypersensitivity. It was noticed
in the clinical trials that about 5% of the patients treated with
abacavir developed a hypersensitivity reaction that resolved
with discontinuation of the drug. The drug manufacturer,
Glaxo Smith Kline, carried out a prospective study and
several retrospective studies and showed a strong association
between HLA-B*5701 and abacavir-induced hypersensitivity
reaction (11). Similar findings were also reported by many
other groups (12–14). Recently, the abacavir label was
updated to reflect the new findings, and genotyping HLA-
B*5701 was recommended before prescribing abacavir.
Prescreening of HIV-1-infected patients for HLA-B*5701
has shown to significantly reduce the number of abacavir
hypersensitivity cases in various parts of the world (11).
Often, a panel of biomarkers is used in clinical practice,for
example, to monitor/test liver function.The objective of using a
panel of biomarkers is to get higher sensitivity and specificity
that represent the association of the biomarker to relevant
clinical events.Forexample,assessment of serumα-fetoprotein
(AFP) used for diagnosis of liver cancer shows a sensitivity of
65% and a specificity of 89% at a cutoff of 30 ng/mL. However,
244Tesch et al.
when a panel of biomarkers, AFP, vascular endothelial growth
sensitivity is 100% and specificity is 95% (15). This is likely to
be true for pharmacogenomic biomarkers, and a panel of
pharmacogenomic biomarkers or a combination of pharmaco-
genomic and other biomarkers may provide higher predictive
power than individual biomarkers.
Future Opportunities and Challenges
Development of biomarkers through analytical and
clinical validation and by demonstrating evidence of clinical
utility is time-consuming, labor-intensive, and financially
challenging. Availability of sufficient number of good quality
samples from which biomarkers can be measured is also a
challenge. Thus, collaborations through consortia are a
feasible path forward to qualify biomarkers in some cases.
In the drug development scenario, the biomarkers that can be
used for increasing the drug's efficacy or safety may be
identified early in development. In addition, in the case of
targeted therapeutics that may work in patient subpopula-
tions, the biomarker to be tested can be qualified early in the
development process also, e.g., in the proof-of-concept study.
In other cases, particularly those related to adverse events
that occur in a very small percentage of the patients or are
idiosyncratic, discoveries and observations of association of
biomarkers might be made either late in the development
process or in the postmarketing stage. Based on the evidence,
drug labels are updated with biomarker data to better inform
health care professionals, thereby providing better patient
Use of diagnostic biomarker tests in clinical disease
management is not new. In fact, it has been reported that
about 70% of the decisions made by physicians for managing
diseases in USA are based on results of diagnostic tests (16).
Novel tests, like MammaPrint for prediction of disease
outcome for breast cancer patients and AlloMap® test for
prediction of acceptance or rejection of heart transplants, will
provide additional help to health care professionals in better
Pharmacogenomic biomarkers have allowed updates of
some drug labels to better patient care. Genomic biomarkers
are evolving to allow prediction of disease onset and
individualized medicine. Proteonomic and metabonomic bio-
markers make it possible to diagnose diseases earlier and
more accurately and to implement precise plans for manage-
ment of disease treatment. However, there are a tremendous
amount of challenges facing health professionals and regu-
latory agencies in treating diseases such as diabetes, osteo-
porosis, rheumatoid arthritis, and many other diseases. As
science advances, better mechanism-based and evidenced-
based biomarkers will be available for diagnosis of disease,
treatment and management of disease, and prediction of
disease onset. A concerted effort between clinicians, regu-
latory agencies, drug developers, and research scientists in
development of biomarkers for clinical utility will importantly
revolutionize heath care.
Standard Biomarkers Used for the Diagnosis of Diabetic
Diabetic nephropathy is usually first diagnosed by the
onset of microalbuminuria (a urine albumin excretion rate of
20–200 μg/min) which may steadily progress to overt albu-
minuria (>200 μg/min) if left unmanaged. Conventional
methods for measuring albuminuria are based on immuno-
detection, which include immunonephelometry, immuno-
turbidimetry, and radioimmunoassay. However, these
techniques underestimate the true level of urine albumin,
because the detection antibody used does not recognize some
forms of albumin, which are modified in the circulation by
ongoing processes such as glycation, lipidation, or oxidative
stress. This problem has been overcome by the development
of a high-performance liquid chromatography-based screen-
ing assay (Accumin test) which detects both immunoreactive
and nonimmunoreactive albumin (17). The increased sensi-
tivity of this technique can predict the onset of overt
albuminuria in diabetic patients 2 to 4 years earlier than
conventional methods. However, the accuracy of Accumin
test in quantifying albumin has been shown to be dependent
on the quality of the calibrators used and may be influenced
by contaminants which co-elute with albumin (18).
Renal function in diabetic patients is commonly esti-
mated by measuring serum creatinine or the rate of creatinine
clearance using the Jaffe assay. However, this technique has
some limitations such as being affected by muscle mass, age,
and gender, and is most sensitive when renal injury is
advanced and the patient glomerular filtration rate (GFR) is
relatively poor (19). In comparison, when GFR is near
normal, renal function can be measured with greater accuracy
and sensitivity in diabetic patients using cystatin-C (20). This
allows earlier detection of declining renal function earlier
when albumin excretion is in the microalbuminuria range.
Novel Biomarkers which Predict the Clinical Progression
of Diabetic Nephropathy
Significant efforts have been made to identify novel
serum and urine biomarkers which can clinically predict and
evaluate diabetic nephropathy. Two promising serum bio-
markers are uric acid and sTNF-R1, both of which are
independently associated with variations in renal function in
type 1 diabetic patients (21). These markers correlate with an
early decline in renal function, prior to the onset of overt
albuminuria, suggesting that they may play a causal role in
renal function impairment, although no definitive mecha-
nisms have yet been established.
A number of urine biomarkers are known to reflect
different components of diabetic renal damage, including
hemodynamic changes, injury to specific cell types, inflamma-
tion, and fibrosis. Elevated intraglomerular hydraulic pressure
is induced by hyperglycemia and is thought to play a pivotal
role in the development of diabetic glomerulosclerosis. This
hemodynamic change is accompanied by increased urine
excretion of immunoglobulin G, transferrin, and ceruloplasmin
in diabetic patients, which precedes the development of micro-
albuminuria, indicating that these proteins are biomarkers of
245Translating Biomarkers into Clinical Applications
early diabetic glomerular injury (22). Cell specific proteins
produced by glomerular podocytes (nephrin) and proximal
tubules (kidney injury molecule-1) are detectedin patient urine
during the development of diabetic nephropathy and are
sensitive markers of glomerular injury and tubulointerstitial
damage, respectively (23,24). Resident kidney cells produce
chemokines in response to stimulation by the diabetic milieu,
which subsequently promotes the development of renal inflam-
mation and tissue injury. Some of these chemokines (monocyte
chemoattractant protein-1, interleukin (IL)-8, IP-10, and mac-
rophage inflammatory protein-1δ) are elevated in the urine of
diabetic patients and correlate with a decline in renal function
(25). Urine chemokine levels appear to reflect the level of
diabetic renal inflammation, which contributes to disease
progression (26). Renal fibrosis in diabetic kidneys is known
to be dependent on transforming growth factor (TGF)-β1;
however, urine levels of this cytokine are low in diabetic
patients, indicating that it is notan analytically sensitive marker
times more abundant in urine and serves as an excellent
measurable marker of TGF-β bioactivity. βig-h3 is elevated
in diabetic patients before the onset of microalbuminuria,
indicating that it is an early marker of fibrosis and renal
Proteomic analysis of urine has been used as an unbiased
method for identifying (1) individual proteins that are selec-
tively increased or decreased in diabetic nephropathy, (2)
protein patterns which are specific indicators of diabetic
nephropathy, and (3) protein patterns which can predict the
progression of diabetic nephropathy. A recent study has
identified the ubiquitin fusion protein (UbA52) as a urine
biomarker that is increased in patients with diabetic nephrop-
athy but is not elevated in otherwise normal patients, diabetics
without nephropathy, or patients with other proteinuric renal
diseases (28). Interestingly, this study also identified a 6.2-kDa
fragment of degraded ubiquitin as a urine protein that was
specifically absent from patients with diabetic nephropathy.
Clinical analysis has also found urine proteomic patterns can
predict the progression of diabetic nephropathy with high
specificity. One report has identified a 12-peak proteomic mass
spectrometer signature that could predict cases of diabetic
nephropathy with 76% specificity (29). Similarly, a more
complex panel of 65 biomarkers has been shown to predict
cases of diabetic nephropathy with 97% specificity and differ-
entiate from other renal diseases with 91% specificity (30). In
this latter study, it was noted that many of the urine biomarkers
identified were fragments of collagen type I that were reduced
in diabetic patients.
Biomarkers for Assessing Therapeutic Efficacy in Diabetic
Currently, most new therapies for diabetic nephropathy
are evaluated by their ability to reduce albuminuria as a
“surrogate” endpoint; however, this approach may not
properly define the therapeutic potential of some treatments.
Diabetic microalbuminuria is not reliable at predicting the
progression to end stage renal disease, and up to 60% of
diabetic patients will show some regression in albuminuria
levels during the course of disease (31). Furthermore, once
overt albuminuria is established, it can be difficult to reverse,
indicating that the processes responsible for albuminuria are
complex. Indeed, albuminuria appears to be influenced by
multiple mechanisms, involving glomerular filtration and
tubular reabsorption, which may differ during the progression
from micro- to overt albuminuria (32). An alternative
approach to evaluating diabetic nephropathy therapies may
be to determine their effectiveness in targeting specific
disease mechanisms such as inflammation, cell injury and
fibrosis, as well as the impact on albuminuria and renal
function. This might be achievable using specific urine
biomarkers (described above) and may determine how and
when the therapy could be best applied; however, this
approach is currently unproven. It is also noteworthy that
biopsy studies have identified accrual of interstitial myofibro-
blasts as one of the best predictors of diabetic nephropathy
progression (33), suggesting that more emphasis should be
placed on developing biomarkers that assess interstitial
Genetic Biomarkers for Determining Susceptibility
to Diabetic Nephropathy
Familial clustering of diabetic nephropathy and ethic
variations in its prevalence indicates that genetic factors
influence the susceptibility to this disease. Genome-wide link-
age scans have suggested several loci (e.g., 1q43, 7q36, 8q21,
and 18q23) are candidates for susceptibility (34–36); however,
none of these appear to be a singularly important determinant
of diabetic nephropathy. Similarly, the analysis of associations
between disease traits and single nucleotide polymorphisms
(SNPs) has so far only identified polymorphisms in candidate
genes (e.g., angiotensin converting enzyme, aldose reductase,
glucose transporter 1, and carnosinase) with small effects
(36,37). The current inability to identify genetic biomarkers,
which can significantly predict diabetic nephropathy in the
general population, may be due to the large variations in racial
and genetic backgrounds and the heterogeneity of disease
phenotypes in the analysis groups. It is hoped that future
developments in molecular biology and advances in the
knowledge of disease mechanisms will provide us with genetic
screening tools which can identify those patients with greatest
risk of developing diabetic nephropathy and those who will
respond best to specific treatments. This may lead to earlier
therapeutic intervention and more customized strategies for
Recent research has seen significant developments in
biomarkers for diabetic nephropathy, particularly in urine
analysis. This has been made possible by advances in the
methods of proteomic analysis and a greater understanding of
the disease mechanisms involved. Current evidence from
small clinical studies indicates that the probability of develop-
ment of diabetic nephropathy may be predicted by evaluating
a combination of serum and urine markers together with
other risk factors such as age, the presence of retinopathy,
and the need for insulin (in type 2 diabetes). However,
clinical studies with greater number of patients are required
to compare sets of biomarkers/risk factors and achieve
agreement on which combination offers the most useful and
246Tesch et al.
cost effective clinical information. Urine proteomic patterns
and genetic biomarkers may also become part of this analysis
in the future. In addition, future evaluation of therapeutic
drugs is likely to involve selection of biomarkers which
specifically reflect the disease mechanism being targeted and
may serve as pharmacodynamic endpoints.
BIOMARKERS IN RHEUMATOID ARTHRITIS:
OVERVIEW AND REGULATORY PERSPECTIVE
Rheumatoid arthritis (RA) is a chronic, destructive,
inflammatory polyarthritis often accompanied by systemic
features. In patients with RA, the disease process results in
impairment in carrying out activities of daily living due to
inflammation and damage to joints. Eventually, accumulated
damage may lead to the need for joint replacement.
Furthermore, there is a growing recognition that patients
with RA are at increased risk of cardiovascular disease.
Drug therapies for RA include symptomatic therapies
and disease modifying antirheumatic drugs (DMARDs).
Symptomatic therapies include nonsteroidal anti-inflammatory
drugs (NSAIDs), hydroxychloroquine, and sulfasalazine. Low
dose corticosteroids are often used to control disease activity,
but their use is limited by well-known adverse effects. The
mechanisms of action of DMARDs are varied and include
antimetabolites (such as methotrexate and leflunomide),
tumor necrosis factor (TNF) blockers (such as infliximab,
etanercept, and adalimumab and, more recently, golimumab
and certolizumab), IL-1 blockade (with anakinra), T cell co-
stimulation blockade (with abatacept), and B cell depletion
(with rituximab). The American College of Rheumatology
(ACR) published treatment guidelines in 2008 (38), recom-
mending that the choice of RA treatment should be based on
the presence or absence of features of poor prognosis, the
level of disease activity, and the duration of disease. The ACR
guidelines recommend nonbiologic DMARDs for patients
with low or moderate disease activity. Biologic DMARDs are
recommended for patients with persistent high disease activity
lasting for 3–6 months or longer, for patients with high disease
activity for a shorter time when they have features of poor
prognosis, or for patients with high disease activity who are
unresponsive to nonbiologic DMARDs. The cost of treatment
and the level of insurance coverage are recognized to impact
the choice of RA treatment regimens as well.
What Are Biomarkers and How Can They Contribute
to Addressing Unresolved Questions in RA Therapy?
Biomarkers are objectively measured biological charac-
teristics that reflect predisposition to disease, disease status,
or response to pharmacologic intervention. They have the
potential to contribute in several important ways to the
treatment of patients with RA. Given the wide array of
therapeutic options, biomarkers could be developed to help
select the optimal treatment for individual patients. This
could represent an important advance over the empiric
method for choosing therapies used today. Biomarkers could
help distinguish those patients most likely to respond to a
particular therapy from those unlikely to respond. For
example, a biomarker could be valuable if it could identify
patients particularly likely to respond to a TNF blocker or to
a B cell depleting agent. There is also precedent for
developing biomarkers to identify patients at particularly
high risk for developing severe adverse events in response to
a particular product to allow clinicians to avoid use of that
product or to proceed with extra caution.
Currently, there are a variety of biomarkers in wide-
spread use in routine care of patients with RA and in clinical
trials. Useful biomarkers generally reflect some important
feature of the pathophysiology of disease. For RA, although
the cause of the disease is unknown, there is a growing
knowledge of the underlying predisposing factors and the
mechanisms that lead to the clinical manifestations. Predis-
position to RA is believed to derive from a combination of
genetic predisposition and environmental factors. Avariety of
immunologic and inflammatory mechanisms is known to
perpetuate the disease process and to damage tissues.
Currently used biomarkers and ones being explored fall into
the general categories of genetic biomarkers, imaging bio-
markers, and biomarkers related to the immune system and
Polymorphisms at several genetic loci have been
shown to be associated with RA. Classical genetic studies
showed an important association between RA and certain
structurally related alleles of the DRB complex of the
major histocompatibility complex (MHC) termed the
“Shared Epitope.” More recently, genome-wide association
studies have confirmed association with the MHC and have
identified several new loci based on SNPs. The new genetic
loci implicated in predisposition to RA include (39) the
& PTPN22 encoding the protein lymphoid tyrosine phospha-
tase that has been shown to inhibit T cell activation,
& TRAF1/C5, a susceptibility locus close to the gene
encoding complement component C5 and the gene
encoding the signaling molecule TNF receptor-associated
& CD40, encoding a member of the TNF receptor superfamily
& TNFAIP3, encoding TNF, alpha-induced protein 3
& STAT4, encoding signal transducer and activator of tran-
& IRF5, encoding interferon regulatory factor 5
& CTLA4, encoding cytotoxic T-lymphocyte antigen 4
Interestingly, a number of these susceptibility loci are not
associated with RA only but have also been implicated in
other autoimmune diseases. Clearly, the preponderance of
genetic loci related to signal transduction reflects something
important about the cause of RA and other autoimmune
diseases. However, it is important to be aware that the newly
identified genetic loci, individually and in the aggregate, only
explain a small portion of the heritability of RA, so there
remains a great deal more to be learned about genetic
susceptibility to RA (39).
Genetic biomarkers do not vary over time for an indi-
vidual; therefore, they would not be useful for assessing the
level of disease activity or response to therapy. Rather,
their usefulness would likely be as markers of an individ-
ual's predisposition to developing disease or as prognostic
247 Translating Biomarkers into Clinical Applications
indicators. They may also permit subsetting of patient
populations into patients who respond differentially to
Joint imaging is useful for diagnosing RA, in under-
standing disease status/progression and in predicting prognosis.
Plain films (x-rays) of hands and feet are examined to assess
whether the disease process has led to erosions of the joint
surface and narrowing of the joint space. Clinical trials utilize
joint space narrowing are assessed by blinded readers and
combinedina singlescore(the total Sharp score) that is usedto
assess joint damage at baseline and over the course of the trial.
Agents active at slowing or halting radiographic progression
reduce theincreaseinSharpscorethat occurs during the course
of the trial compared to untreated controls. Another imaging
modality that may prove useful as a biomarker in clinical trials
is magneticresonance imaging (MRI). MRI is a highly sensitive
technique that reveals erosions and inflammation. Efforts are
underway to standardize MRI using the RAMRIS scoring
system. MRI has shown promise not only in assessing the status
of a joint at one point in time but also in determining response
to treatments. For example, MRI has been able to demonstrate
reduction in inflammation in response to TNF blockade in a
very short timeframe (40). Ultrasound is another imaging
modality that may prove utility as a biomarker of RA disease
activity and progression.
Since RA is an autoimmune disease characterized by
inflammation, markers of immunity and inflammation offer
another potential source of useful biomarkers. Biomarkers of
immunity include immune cells (circulating levels of B and T
cells and specific subsets), autoantibodies, and cytokines. A
variety of immune cells is involved in RA progression and
initiation, including T cells, B cells, and macrophages. The
autoantibodies implicated in RA include rheumatoid factor
(RF), which is an antibody to immunoglobulin and anti-
citrullinated protein antibodies (ACPA). A variety of cyto-
kines is involved in RA pathogenesis, including TNF-alpha,
IL-1, IL-6, and others. RF and ACPA are important for
diagnosing RA and as predictors of poor prognosis; however,
they may not be useful for assessing disease activity or
response to therapy since there is no established correlation
between the levels of these autoantibodies with disease
activity. An emerging area for assessing the state of the
immune system in RA and other autoimmune diseases is
measurement of gene expression in peripheral blood by
microarray analysis. Markers of inflammation include acute
phase reactants such as erythrocyte sedimentation rate as well
as C-reactive protein, which are well-established biomarkers
both in clinical practice and in clinical trials. Other soluble
biomarkers that reflect inflammation and tissue damage in
joint and bone include collagen degradation products, matrix
metalloproteinases, receptor activator of nuclear factor kappa
B ligand, and osteoprotegerin. Another marker of inflamma-
tion is the measurement of circulating levels of soluble
receptors, such as soluble TNF receptor.
Use of Biomarkers in RA Drug Development
Qualification of RA biomarkers. Biomarkers can be used
in a variety of different ways in clinical trials. They can be
used as an early measure of clinical activity or as a guide in
drug development to make go/no go decisions. They can be
used to determine optimal dosing of a new drug. They can be
used to select patients most likely to respond to therapy or to
select patients at risk of toxicity. Finally, in select situations,
they can be used as surrogate markers to assess efficacy.
Whether a particular use of a biomarker in a clinical trial is
appropriate depends on whether its level of qualification is
adequate for that specific purpose (termed “fitness for use”).
For example, use in early decision-making in drug
development, such as go–no go decisions, may require only
a relatively low level of qualification. Selecting patients for
phase 3 clinical trials requires a higher level of evidentiary
data. Use of biomarkers as surrogate markers of efficacy in
phase 3 trials necessitates the highest level of validation.
RA Biomarkers in Clinical Trials: Some Considerations
For biomarkers to be used in clinical trials, biomarkers
should have been standardized and qualified in multicenter
experience and shown to reliably correlate with disease
status. If a biomarker is intended to be used in prescribing
the drug when it is approved, the developer should consult
the appropriate FDA center (CDRH or CBER) during
Surrogate markers are a subset of biomarkers that have
been shown to predict clinical benefit; for example, blood
pressure for antihypertensives as a surrogate for strokes and
myocardial infarction or viral titers in HIV disease as a
surrogate for progression to AIDS or development of
opportunistic infections. Therapeutic effects on validated
surrogate endpoints can substitute in many situations for
clinical endpoints in clinical trials of efficacy. Surrogate
endpoints are particularly valuable when (1) they are clearly
qualified as outcome measures, such as blood pressure or
hypertension trials and viral load for HIV, and when (2)
relevant clinical outcomes are not measurable in the time-
frame of clinical trials, for example, complete remission/
partial remission versus survival for oncology trials and long-
term functional outcomes in RA. In the case of serious or life-
threatening disease, if a surrogate marker is not fully
validated but is considered reasonably likely to predict
clinical benefit, then it may be used to assess efficacy under
the provisions of accelerated approval. In this case, there is a
requirement for studies to validate the surrogate marker
postapproval. In the case of RA drug development, the
usefulness of biomarkers as surrogate markers of efficacy is
not clear since clinical outcome measures are sensitive to drug
effects in the time frame of a clinical trial.
Biomarkers may be particularly useful in clinical trials as
part of an enrichment design. For example, rather than
enrolling all comers, a clinical trial may select patients based
on those most likely to response or those most likely to
tolerate the drug and can target therapy to those most likely
to benefit. Enrichment clinical trial designs could include
randomizing patients with a gene signature on microarray
248 Tesch et al.
that has been demonstrated to be predictive of drug
responsiveness or randomizing patients based on a predictive
biomarker such as anti-citrullinated protein antibodies
(ACPA) status. However, there are some caveats to such
enrichment designs. Efficacy observed in enrichment designs
may not be generalizable to the whole patient population. If
efficacy is only shown in a subset of patients, that might need
to be reflected in the drug label. Generally, there should be
good evidence that the criteria used for selection represent a
clinically meaningful way to categorize patients. Further-
more, if it is likely that patients not belonging to a selected
subpopulation will also take the drug, it is important to
study the efficacy and safety of the drug in these patients as
well. One approach to address issues of generalizability
would be to conduct one trial in the enriched population
and one in the general population. If qualitatively similar
results are seen in the enriched and unenriched popula-
tions, this would suggest that the efficacy is not restricted to
the selected population.
Biomarkers have the potential to facilitate drug devel-
opment in RA and other rheumatic diseases. Biomarkers
may be useful at various stages of drug development; for
example, dose selection, assessment of clinical benefits, and
selection of the target population. There are a variety of
promising biomarkers that may prove to have utility in RA
clinical trials. However, prior to their implementation in
clinical trials, biomarkers should undergo an appropriate
process to demonstrate that they have been appropriately
qualified with regard to their role in clinical trials (“fitness
CLINICAL USE OF MARKERS OF BONE
TURNOVER IN FRACTURE RISK ASSESSMENT
Fractures related to osteoporosis continue to be a
substantial and growing public health problem. At the age
of 60, Caucasian men and women, respectively, have a 26%
and 44% chance of suffering a fracture related to osteopo-
rosis during their remaining lifetime (41). Only one third of
those who have a hip fracture regain their prefracture ability
to walk. Clinical vertebral fractures are a common cause of
physical disfigurement, often result in chronic back pain (42),
and can reduce pulmonary function. Fractures related to
osteoporosis were estimated to have a direct medical cost of
$16 billion in 2005 in the USA, and that cost is projected to
rise to $25 billion by 2025 (43).
A variety of medications have become available over the
past 13 years to treat patients who are at high risk of fracture,
beginningwith the marketrelease of alendronatein November
1995 (44). These medications prevent fractures in part by
reducing bone metabolic activity called bone turnover.
Markers that reflect both aspects of bone turnover, bone
formation, and bone resorption are now commercially avail-
able for use in clinical practice. However, use of markers of
bone turnover has not become widespread in the management
What are Markers of Bone Turnover?
The two main classes of bone turnover markers are
protein products of bone type I collagen degradation or
synthesis and enzymes produced with the activities of
osteoblasts and osteoclasts (45). Markers of bone formation
reflect osteoblastic activity, and markers of bone resorption
reflect osteoclast activation.
Markers of Bone Resorption
When bone is resorbed, the N-terminal and C-terminal
products of bone type 1 collagen peptide chains, called N-
telopeptides (NTX) and C-telopeptides (CTX), are released
and can be measured in either serum or urine (Table I) (46).
Cross-links between lysine and hydroxylysine on separate
peptide chains are also present in mature type 1 bone
collagen, remain present in the fragments of type 1 bone
collagen degradation, and can be measured in either serum or
urine (47). The enzyme tartrate resistant acid phosphatase
(TRACP) is synthesized and released with osteoclastic
activation. Serum levels and urinary excretion of these
markers show circadian rhythm and are highest in the early
Table I. Major Biomarkers of Bone Turnover
BiomarkerType Source MeasurementPrecision
C-Telopeptide (CTX)ResorptionType I collagen degradation ELISA (urine, serum)
ELISA (urine, serum)
N-Telopeptide (NTX)Resorption Type I collagen degradation
Deoxypyridinoline (DPD)Resorption Type I collagen degradationELISA (urine) HPLC
ELISA (serum) Tartrate resistant acid phosphatase
Bone alkaline phosphatase
Procollagen type I N-terminal
Resorption Enzyme secreted by osteoclasts2.2%
Enzyme secreted by osteoblasts
Type I collagen synthesis
ELISA (urine) HPLC
8–63% Formation Bone matrix component synthesized
Adapted from reference (46) by Seibel et al.
249 Translating Biomarkers into Clinical Applications
morning (3 to 7:00 a.m.) and fall to a nadir in the early
afternoon hours (48). The difference between the nadir and
peak levels is often as much as 50% of their 24-h mean level.
Bone marker levels need to be assessed in a fasting state, as
food intake tends to decrease bone resorption markers. The
clinical assays of bone resorption markers have become
increasingly automated and efficient with coefficients of
variation now below 10% (49).
Markers of Bone Formation
Procollagen 1 N-terminal propeptide and procollagen 1-
C-terminal propeptide directly reflect bone formation activity
(50). Bone alkaline phosphatase is an enzyme produced by
activated osteoblasts that appears to have a role in calcium
hydroxyapatite deposition on bone. Osteocalcin is a bone
matrix component manufactured by osteoblasts but also
released from bone during bone resorption and, hence,
reflects both osteoblastic activation and bone resorption
Markers of bone formation have less diurnal variation
than markers of bone resorption (51). The diurnal variation
of serum osteocalcin is higher perhaps due to the fact it
reflects both bone formation and bone resorption. Food
intake has a lower effect on bone formation markers than on
bone resorption markers (52).
Changes in Bone Marker Levels Over the Lifespan
Bone metabolism during childhood and adolescence is
dominated by bone formation, and bone formation markers
are higher than in adult premenopausal women (53). In
middle-aged women during the time of transition to meno-
pause, bone resorption activity begins to increase, and the
balance of resorption versus formation tilts in favor of
resorption. This often results in rapid bone loss during the
early postmenopausal period (50).
Bone turnover marker levels increase substantially
within a couple of weeks after acute fractures and can stay
elevated up to a year or more following a fracture (54).
degree but has a variable effect on bone resorption markers
(55). Conversely, immobilization significantly increases bone
resorption (56). Systemic glucocorticoid medications initially
raise but subsequently depress markers of bone formation (57).
Clinical Uses of Bone Marker Turnover Measurement
Assess Risks of Bone Loss and Fractures
Baseline levels of serum and urine levels of osteocalcin
(58,59), TRACP (59), urine CTX, and bone alkaline phos-
phatase (58) are modestly correlated with subsequent femoral
neck bone loss. Three large prospective epidemiologic studies
reported that baseline urine CTX was associated with subse-
quent fractures independent of bone mineral density. Garnero
et al. showed that among elderly women with a femoral neck T-
score of −2.5 or lower, hip fracture incidence was doubled in
those with a urine CTX or urine deoxypyridinoline more than
two standard deviations above the premenopausal mean value
(60). In the Hawaii Osteoporosis Study, baseline urine CTX
and bone alkaline phosphatase were both associated with
subsequent clinical fractures among postmenopausal women
and with clinical and radiographic vertebral fractures among
older postmenopausal participants (61). Gerdhem et al. found
that baseline serum TRACP and urinary long osteocalcin
fragments were associated with subsequent vertebral fracture
but that baseline serum CTX were not associated with
for bone mineral density (62).
Predict Response to Drug Therapy
The standard way to assess the effectiveness of pharma-
cologic fracture prevention therapy has been to measure bone
mineral density one and/or 2 years after the start of therapy.
This has been unsatisfactory because of the long delay
between initiation of therapy and assessment of the effective-
ness of that therapy and because changes in bone mineral
density on fracture prevention therapies are only mildly
correlated with fracture reduction efficacy (63,64). Short-term
changes in bone turnover marker levels appear to predict
response to fracture prevention agents. Greenspan et al.
noted that decreases in serum NTX of >30% and of serum
CTX >50%, respectively, during the first 6 months of alendro-
nate therapy were associated with a significantly greater
osteocalcin are similarly predictive of bone mineral density
terminal propeptide increase seems to be strongly associated
with the bone mineral density increases (67).
In two large randomized controlled trials, the vertebral
fracture risk reduction was correlated in a linear fashion with
decreases of urine NTX down to 35% below baseline and
with decreases of urine CTX down to 60% below baseline.
Further reductions of either NTX or CTX were not associ-
ated with any additional vertebral fracture prevention (68).
Moreover, a single level of bone resorption measured after 3
to 6 months of drug therapy was significantly associated with
vertebral fracture risk reduction on risedronate over 3 years
(68). In contrast, in randomized controlled trials of alendro-
nate (69), risedronate (70), raloxifene, or teriparatide (71)
versus placebo, baseline bone turnover marker levels were not
associated with the magnitude of fracture risk reduction.
Importantly, because of how bone resorption and bone
formation are coupled, markers of bone resorption or forma-
tion can be used to monitor response to either antiresorptive or
anabolic fracture prevention medications. While markers of
boneresorptiondecrease the fastestafter commencement of an
antiresorptive agent, decreases of bone formation follow within
rise first with teriparatide therapy, markers of bone resorption
start to increase a couple of months later (72). Among the bone
formation markers, procollagen 1 N-terminal propeptide
increases more dramatically on teriparatide therapy than bone
alkaline phosphatase and is the preferred test with which to
monitor response to teriparatide therapy (72).
Detect and Improve Compliance with Drug Therapy
A major impediment to reduction of the burden of
fractures related to osteoporosis is noncompliance with
250Tesch et al.
fracture prevention medications (73). Significant changes in
bone markers are expected after commencement of these
agents if the patients comply with the prescription, and the
lack of such a change within approximately 3 months of
initiating fracture prevention therapy can aid identification of
those who are not complying with therapy (74). That may be
a good entry point for the practitioners to engage those
patients to improve their compliance by addressing the
patients' concerns regarding the long-term safety of those
medications and by assessing whether or not the patient's
perceptions of the risk and benefit of drug therapy are
appropriate and realistic.
The measurement of markers of bone turnover has
improved substantially over the past decade and is increas-
ingly important in routine clinical practice in the management
of osteoporosis and treatment of those at high risk of fracture.
In particular, measurement of baseline and follow-up bone
turnover markers can accurately assess whether or not the
patient is experiencing the expected biologic response to the
drug. Moreover, this may help in the identification of those
who are nonpersistent or noncompliant with their prescribed
fracture prevention medication. Bone turnover markers may
have a modest role to play in fracture risk assessment.
However, further studies of the associations of baseline bone
markers drawn under appropriate conditions (fasting state
and in the early morning) are needed to better establish what,
if any, additional information bone marker measurements
may give regarding fracture risk beyond what can be gleaned
from bone densitometry and other clinical risk factors. At this
time, there are insufficient data to support the use of bone
marker measurements in individual patients to predict future
The authors would like to thank Drs. Issam Zineh
(Associate Director of Genomics Group, Office of Clinical
Pharmacology, CDER, FDA) and Federico Goodsaid (Asso-
ciate Director of Operation of Genomics Group, Office of
Clinical Pharmacology, CDER, FDA) for their comments on
themanuscript.The authors would like tothank Dr. Jean Lee at
Disclaimer The views expressed in this article do not necessarily
represent the views of the US Food and Drug Administration.
1. Innovation or stagnation? Challenge and opportunity on the
critical path to new medical products. http://www.fda.gov/oc/
3. Innovation or stagnation? Critical path opportunities list. http://
4. Guidance for industry, pharmacogenomic data submissions.
5. Orr MS, Goodsaid F, Amur S, Rudman A, Frueh FW. The
experience with voluntary genomic data submissions at the FDA
and a vision for the future of the voluntary data submission
program. Clin Pharmacol Ther. 2007;81(2):294–7.
6. Goodsaid F, Frueh F. Biomarker qualification pilot process at the
US Food and Drug Administration. Aaps J. 2007;9(1):E105–8.
7. FDA and EMEA conclude that new renal safety biomarkers are
qualified for specific regulatory purposes. http://www.c-path.org/
8. Approved label of Maraviroc. http://www.fda.gov/cder/foi/label/
9. Approved label of Trastuzumab. http://www.fda.gov/cder/foi/
10. Approved label of azathioprine. http://www.fda.gov/cder/foi/
11. Hughes AR, Spreen WR, Mosteller M, et al. Pharmacogenetics
of hypersensitivity to abacavir: from PGx hypothesis to con-
firmation to clinical utility. Pharmacogenomics J. 2008;8(6):365–
12. Mallal S, Phillips E, Carosi G, et al. HLA-B*5701 screening for
hypersensitivity to abacavir. N Engl J Med. 2008;358(6):568–79.
13. Waters LJ, Mandalia S, Gazzard B, Nelson M. Prospective HLA-
B*5701 screening and abacavir hypersensitivity: a single centre
experience. Aids. 2007;21(18):2533–4.
14. Rodriguez-Novoa S, Garcia-Gasco P, Blanco F, et al. Value of the
HLA-B*5701 allele to predict abacavir hypersensitivity in
Spaniards. AIDS Res Hum Retroviruses. 2007;23(11):1374–6.
15. el-Houseini ME, Mohammed MS, Elshemey WM, Hussein TD,
Desouky OS, Elsayed AA. Enhanced detection of hepatocellular
carcinoma. Cancer Control. 2005;12(4):248–53.
16. Fang KC. Clinical utilities of peripheral blood gene expression
profiling in the management of cardiac transplant patients. J
17. Comper WD, Jerums G, Osicka TM. Differences in urinary
albumin detected by four immunoassays and high-performance
liquid chromatography. Clin Biochem. 2004;37(2):105–11.
18. Shaikh A, Seegmiller JC, Borland TM, et al. Comparison
between immunoturbidimetry, size-exclusion chromatography,
and LC-MS to quantify urinary albumin. Clin Chem. 2008;54
19. Rule AD. Understanding estimated glomerular filtration rate:
implications for identifying chronic kidney disease. Curr Opin
Nephrol Hypertens. 2007;16(3):242–9.
20. Mussap M, Dalla Vestra M, Fioretto P, et al. Cystatin C is a more
sensitive marker than creatinine for the estimation of GFR in
type 2 diabetic patients. Kidney Int. 2002;61(4):1453–61.
21. Rosolowsky ET, Niewczas MA, Ficociello LH, Perkins BA,
Warram JH, Krolewski AS. Between hyperfiltration and impair-
ment: demystifying early renal functional changes in diabetic
nephropathy. Diabetes Res Clin Pract. 2008;82 Suppl 1:S46–53.
22. Narita T, Hosoba M, Kakei M, Ito S. Increased urinary
excretions of immunoglobulin G, ceruloplasmin, and transferrin
predict development of microalbuminuria in patients with type 2
diabetes. Diabetes Care. 2006;29(1):142–4.
23. Patari A, Forsblom C, Havana M, Taipale H, Groop PH,
Holthofer H. Nephrinuria in diabetic nephropathy of type 1
diabetes. Diabetes. 2003;52(12):2969–74.
24. van Timmeren MM, van den Heuvel MC, Bailly V, Bakker SJ,
van Goor H, Stegeman CA. Tubular kidney injury molecule-1
(KIM-1) in human renal disease. J Pathol. 2007;212(2):209–17.
25. Wolkow PP, Niewczas MA, Perkins B, et al. Association of
urinary inflammatory markers and renal decline in microalbumi-
nuric type 1 diabetics. J Am Soc Nephrol. 2008;19(4):789–97.
target for progressive renal injury in diabetic nephropathy. Am J
Physiol Renal Physiol. 2008;294(4):F697–701.
27. Ha SW, Kim HJ, Bae JS, et al. Elevation of urinary betaig-h3,
transforming growth factor-beta-induced protein in patients with
type 2 diabetes and nephropathy. Diabetes Res Clin Pract.
28. Dihazi H, Muller GA, Lindner S, et al. Characterization of
diabetic nephropathy by urinary proteomic analysis: identifica-
tion of a processed ubiquitin form as a differentially excreted
protein in diabetic nephropathy patients. Clin Chem. 2007;53
251Translating Biomarkers into Clinical Applications
29. Otu HH, Can H, Spentzos D, et al. Prediction of diabetic
nephropathy using urine proteomic profiling 10 years prior to
development of nephropathy. Diabetes Care. 2007;30(3):638–
30. Rossing K, Mischak H, Dakna M, et al. Urinary proteomics in
diabetes and CKD. J Am Soc Nephrol. 2008;19(7):1283–90.
31. Perkins BA, Ficociello LH, Silva KH, Finkelstein DM, Warram
JH, Krolewski AS. Regression of microalbuminuria in type 1
diabetes. N Engl J Med. 2003;348(23):2285–93.
32. Comper WD, Hilliard LM, Nikolic-Paterson DJ, Russo LM.
Disease-dependent mechanisms of albuminuria. Am J Physiol
Renal Physiol. 2008;295(6):F1589–600.
33. Essawy M, Soylemezoglu O, Muchaneta Kubara EC, Shortland
J, Brown CB, el Nahas AM. Myofibroblasts and the progression
of diabetic nephropathy. Nephrol Dial Transplant. 1997;12
34. Freedman BI, Bostrom M, Daeihagh P, Bowden DW. Genetic
factors in diabetic nephropathy. Clin J Am Soc Nephrol. 2007;2
35. Schelling JR, Abboud HE, Nicholas SB, et al. Genome-wide scan
for estimated glomerular filtration rate in multi-ethnic diabetic
populations: the Family Investigation of Nephropathy and
Diabetes (FIND). Diabetes. 2008;57(1):235–43.
36. Rogus JJ, Poznik GD, Pezzolesi MG, et al. High-density single
nucleotide polymorphism genome-wide linkage scan for suscept-
ibility genes for diabetic nephropathy in type 1 diabetes:
discordant sibpair approach. Diabetes. 2008;57(9):2519–26.
37. Granier C, Makni K, Molina L, Jardin-Watelet B, Ayadi H,
Jarraya F. Gene and protein markers of diabetic nephropathy.
Nephrol Dial Transplant. 2008;23(3):792–9.
38. Saag KG, Teng GG, Patkar NM, et al. American College of
Rheumatology 2008 recommendations for the use of nonbiologic
and biologic disease-modifying antirheumatic drugs in rheuma-
toid arthritis. Arthritis Rheum. 2008;59(6):762–84.
39. van der Helm-van Mil AH, Padyukov L, Toes RE, Klareskog L,
Huizinga TW. Genome-wide single-nucleotide polymorphism
studies in rheumatology: hype or hope? Arthritis Rheum.
40. Conaghan PG, Quinn MA, O'Connor P, Wakefield RJ, Karim Z,
Emery P. Can very high-dose anti-tumor necrosis factor blockade
at onset of rheumatoid arthritis produce long-term remission?
Arthritis Rheum. 2002;46(7):1971–2. author reply 1973.
41. Nguyen ND, Ahlborg HG, Center JR, Eisman JA, Nguyen TV.
Residual lifetime risk of fractures in women and men. J Bone
Miner Res. 2007;22(6):781–8.
42. Silverman SL, Minshall ME, Shen W, Harper KD, Xie S. The
relationship of health-related quality of life to prevalent and
incident vertebral fractures in postmenopausal women with
osteoporosis: results from the Multiple Outcomes of Raloxifene
Evaluation Study. Arthritis Rheum. 2001;44(11):2611–9.
43. Burge R, Dawson-Hughes B, Solomon DH, Wong JB, King A,
Tosteson A. Incidence and economic burden of osteoporosis-
related fractures in the United States, 2005-2025. J Bone Miner
44. MacLean C, Newberry S, Maglione M, et al. Systematic review:
comparative effectiveness of treatments to prevent fractures in
men and women with low bone density or osteoporosis. Ann
Intern Med. 2008;148(3):197–213.
45. Datta HK, Ng WF, Walker JA, Tuck SP, Varanasi SS. The cell
biology of bone metabolism. J Clin Pathol. 2008;61(5):577–87.
46. Seibel MJ, Eastell R, Gundberg CM, Hannon R, Pols HAP.
Biochemical markers of bone metabolism. In: Bilezikian JP,
Raisz LG, Rodan GA, editors. Principles of bone biology. San
Diego: Academic; 2002. p. 1543–71.
47. Robins SPBJ, Bilezikian J. Collagen cross-linking and metabo-
lism. In: Bilezikian JP, Raisz LG, Rodan GA, editors. Principles
of bone biology. San Diego: Academic; 2002. p. 211–24.
48. Wichers M, Schmidt E, Bidlingmaier F, Klingmuller D. Diurnal
rhythm of CrossLaps in human serum. Clin Chem. 1999;45
49. Claudon A, Vergnaud P, Valverde C, Mayr A, Klause U,
Garnero P. New automated multiplex assay for bone turnover
markers in osteoporosis. Clin Chem. 2008;54(9):1554–63.
50. Eastell R, Hannon RA. Biomarkers of bone health and
osteoporosis risk. Proc Nutr Soc. 2008;67(2):157–62.
51. Samoszuk M, Leuther M, Hoyle N. Role of serum P1NP
measurement for monitoring treatment response in osteoporosis.
Biomarkers Med. 2008;2(5):495–508.
52. Clowes JA, Allen HC, Prentis DM, Eastell R, Blumsohn A.
Octreotide abolishes the acute decrease in bone turnover in
response to oral glucose. J Clin Endocrinol Metab. 2003;88
53. Glover SJ, Garnero P, Naylor K, Rogers A, Eastell R. Establish-
ing a reference range for bone turnover markers in young,
healthy women. Bone. 2008;42(4):623–30.
54. Obrant KJ, Ivaska KK, Gerdhem P, Alatalo SL, Pettersson K,
Vaananen HK. Biochemical markers of bone turnover are
influenced by recently sustained fracture. Bone. 2005;36(5):786–
55. Adami S, Gatti D, Viapiana O, et al. Physical activity and bone
turnover markers: a cross-sectional and a longitudinal study.
Calcif Tissue Int. 2008;83(6):388–92.
56. Inoue M, Tanaka H, Moriwake T, Oka M, Sekiguchi C, Seino Y.
Altered biochemical markers of bone turnover in humans during
120 days of bed rest. Bone. 2000;26(3):281–6.
57. Ardissone P, Rota E, Durelli L, Limone P, Isaia GC. Effects of
high doses of corticosteroids on bone metabolism. J Endocrinol
58. Chapurlat RD, Garnero P, Sornay-Rendu E, Arlot ME, Claustrat
B, Delmas PD. Longitudinal study of bone loss in pre- and
perimenopausal women: evidence for bone loss in perimeno-
pausal women. Osteoporos Int. 2000;11(6):493–8.
59. Lenora J, Ivaska KK, Obrant KJ, Gerdhem P. Prediction of bone
loss using biochemical markers of bone turnover. Osteoporos Int.
60. Garnero P, Hausherr E, Chapuy MC, et al. Markers of bone
resorption predict hip fracture in elderly women: the EPIDOS
Prospective Study. J Bone Miner Res. 1996;11(10):1531–8.
61. Garnero P, Sornay-Rendu E, Claustrat B, Delmas PD. Bio-
chemical markers of bone turnover, endogenous hormones and
the risk of fractures in postmenopausal women: the OFELY
study. J Bone Miner Res. 2000;15(8):1526–36.
62. Gerdhem P, Ivaska KK, Isaksson A, et al. Associations
between homocysteine, bone turnover, BMD, mortality, and
fracture risk in elderly women. J Bone Miner Res. 2007;22
63. Hochberg MC, Greenspan S, Wasnich RD, Miller P, Thompson
DE, Ross PD. Changes in bone density and turnover explain the
reductions in incidence of nonvertebral fractures that occur
during treatment with antiresorptive agents. J Clin Endocrinol
64. Chapurlat RD, Palermo L, Ramsay P, Cummings SR. Risk of
fracture among women who lose bone density during treatment
with alendronate. The Fracture Intervention Trial. Osteoporos
65. Greenspan SL, Parker RA, Ferguson L, Rosen HN, Maitland-
Ramsey L, Karpf DB. Early changes in biochemical markers of
bone turnover predict the long-term response to alendronate
therapy in representative elderly women: a randomized clinical
trial. J Bone Miner Res. 1998;13(9):1431–8.
66. Ravn P, Hosking D, Thompson D, et al. Monitoring of
alendronate treatment and prediction of effect on bone mass
by biochemical markers in the early postmenopausal inter-
vention cohort study. J Clin Endocrinol Metab. 1999;84(7):
67. Bauer DC, Garnero P, Bilezikian JP, et al. Short-term changes in
bone turnover markers and bone mineral density response to
parathyroid hormone in postmenopausal women with osteopo-
rosis. J Clin Endocrinol Metab. 2006;91(4):1370–5.
68. Eastell R, Barton I, Hannon RA, Chines A, Garnero P, Delmas
PD. Relationship of early changes in bone resorption to the
reduction in fracture risk with risedronate. J Bone Miner Res.
69. Bauer DC, Garnero P, Hochberg MC, et al. Pretreatment levels
of bone turnover and the antifracture efficacy of alendronate: the
fracture intervention trial. J Bone Miner Res. 2006;21(2):292–9.
70. Seibel MJ, Naganathan V, Barton I, Grauer A. Relationship
between pretreatment bone resorption and vertebral fracture
incidence in postmenopausal osteoporotic women treated with
risedronate. J Bone Miner Res. 2004;19(2):323–9.
252Tesch et al.
71. Delmas PD, Licata AA, Reginster JY, et al. Fracture risk Download full-text
reduction during treatment with teriparatide is independent of
pretreatment bone turnover. Bone. 2006;39(2):237–43.
72. Chen P, Satterwhite JH, Licata AA, et al. Early changes in
biochemical markers of bone formation predict BMD response
to teriparatide in postmenopausal women with osteoporosis. J
Bone Miner Res. 2005;20(6):962–70.
73. Kothawala P, Badamgarav E, Ryu S, Miller RM, Halbert RJ.
therapy for osteoporosis. Mayo Clin Proc. 2007;82(12):1493–501.
74. Delmas PD, Vrijens B, Eastell R, et al. Effect of monitoring bone
turnover markers on persistence with risedronate treatment of
postmenopausal osteoporosis. J Clin Endocrinol Metab. 2007;92
253 Translating Biomarkers into Clinical Applications