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Identifying novel clinical surrogates to assess human bone fracture toughness
Mathilde Granke1,2, Alexander J Makowski1,2,3,4, Sasidhar Uppuganti1,2, Mark D Does4,5,6,7, and Jeffry S Nyman1,2,3,4
1Department of Orthopaedics Surgery & Rehabilitation, Vanderbilt University Medical Center, Nashville, TN 37232
2Center for Bone Biology, Vanderbilt University Medical Center, Nashville, TN 37232
3Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN 37212
4Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232
5Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232
6Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232
7Department of Electrical Engineering, Vanderbilt University, Nashville, TN 37232
Abstract
Fracture risk does not solely depend on strength but also on fracture toughness, i.e. the ability of
bone material to resist crack initiation and propagation. Because resistance to crack growth largely
depends on bone properties at the tissue level including collagen characteristics, current X-ray
based assessment tools may not be suitable to identify age-, disease-, or treatment-related changes
in fracture toughness. To identify useful clinical surrogates that could improve the assessment of
fracture resistance, we investigated the potential of 1H nuclear magnetic resonance spectroscopy
(NMR) and reference point indentation (RPI) to explain age-related variance in fracture toughness.
Harvested from cadaveric femurs (62 human donors), single-edge notched beam (SENB)
specimens of cortical bone underwent fracture toughness testing (R-curve method). NMR-derived
bound water showed the strongest correlation with fracture toughness properties (r=0.63 for crack
initiation, r=0.35 for crack growth, and r=0.45 for overall fracture toughness; p<0.01).
Multivariate analyses indicated that the age-related decrease in different fracture toughness
properties were best explained by a combination of NMR properties including pore water and
RPI-derived tissue stiffness with age as a significant covariate (adjusted R2 = 53.3%, 23.9%, and
35.2% for crack initiation, crack growth, and overall toughness, respectively; p<0.001). These
findings reflect the existence of many contributors to fracture toughness and emphasize the utility
Contact: Jeffry S. Nyman, Medical Center East, South Tower, Suite 4200, Nashville, TN 37232, [email protected], office: (615) 936-6296, fax: (615) 936-0117.
Disclosures: The authors have no conflict of interest to declare.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other funding agencies.
Authors’ roles: Study design: MG, AJM, MDD, JSN. Study conduct: MG, AJM, SU, JSN. Data collection: MG, SU, AJM, JSN. Data analysis: MG, SU, JSN. Data interpretation: MG, MDD, JSN. Drafting manuscript: MG, MDD, JSN. Approving final version of manuscript: MG, AJM, SU, MDD, JSN. JSN takes responsibility for the integrity of the data analysis.
HHS Public AccessAuthor manuscriptJ Bone Miner Res. Author manuscript; available in PMC 2016 July 01.
Published in final edited form as:J Bone Miner Res. 2015 July ; 30(7): 1290–1300. doi:10.1002/jbmr.2452.
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of a multimodal assessment of fracture resistance. Exploring the mechanistic origin of fracture
toughness, glycation-mediated, non-enzymatic collagen crosslinks and intra-cortical porosity are
possible determinants of bone fracture toughness and could explain the sensitivity of NMR to
changes in fracture toughness. Assuming fracture toughness is clinically important to the ability of
bone to resist fracture, our results suggest that improvements in fracture risk assessment could
potentially be achieved by accounting for water distribution (quantitative ultrashort echo-time
magnetic resonance imaging) and by a local measure of tissue resistance to indentation (RPI).
Keywords
fracture toughness; human cortical bone; nuclear magnetic resonance; bound water; reference point indentation; non-enzymatic collagen cross-links; porosity
Introduction
Fracture resistance of bone depends on yield strength, the ability of bone to withstand high
force or stress without appreciable permanent deformation as well as fracture toughness, the
ability of bone to resist crack initiation and propagation. These two attributes describe
different aspects of the mechanical behavior of bone and, as such, high yield strength does
not necessarily mean low or high fracture toughness.(1) Reduced bone strength typically
results from a loss of bone mass and correlates well with areal bone mineral density (aBMD)
as measured by dual-energy X-ray absorptiometry (DXA), the clinical gold standard to
assess fracture risk. Fractures however are not solely the manifestation of low bone strength
or low aBMD.(2) For fractures to occur, damage must form and propagate. Fracture
resistance thus depends on the ability of bone to resist damage initiation, accumulation, and
propagation. While strength characterizes some of this ability, there are other mechanical
properties – fatigue life, toughness, and fracture toughness – that specifically assess various
damaging mechanisms of energy dissipation during fracture. The idea that fracture
toughness at the apparent level could also significantly contribute to the overall fracture
resistance is gaining prominence in recent years as an apparent compromise in fracture
toughness mechanisms could underpin the occurrence of atypical femoral fractures(3,4) and
may explain why patients with type 2 diabetes are at higher risk of fracture despite a normal
aBMD.(5,6)
There are multiple ways to characterize the fracture toughness of a material. Whether
determined as the critical stress state beyond which the crack begins to grow (stress intensity
factor K), the non-linear elastic energy dissipated prior to and during fracture (J-integral), or
the toughness evolution with crack extension (crack resistance curve or R-curve), fracture
toughness of bone decreases with advancing age.(7–10) Like strength, it is related to apparent
bone density,(11,12) but fracture toughness also largely depends on microstructural and
compositional properties at the material level such as tissue orientation,(12–14) cement line
properties,(15) osteon density,(16) tissue heterogeneity,(17) water content,(11,18) accumulation
of in vivo microdamage,(19,20) or accumulation of advanced glycation end-products
(AGEs).(8,21) As X-rays are insensitive to numerous deleterious changes within bone tissue,
bone densitometry may not be suitable to identify age-, disease-, or treatment-related
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changes in fracture toughness. This would explain to some extent the lack of specificity of
aBMD in identifying certain individuals at imminent risk of a devastating fracture. (22–24)
Therefore, we investigated the potential of two clinically translatable technologies to
become surrogates of fracture toughness assessment. First, 1H nuclear magnetic resonance
spectroscopy (NMR) allows one to evaluate the concentration of water interacting with the
matrix (bound water) and water residing in pores (pore water).(25–27) Hydration and porosity
are known determinants of fracture toughness of bone(11,18) and are quantifiable by clinical
magnetic resonance imaging (MRI).(28,29) The second technology is reference point
indentation (RPI),(30,31) an instrument designed for clinical measurements of bone material
properties by indenting a patient’s tibia. In particular, RPI could be sensitive to some of the
intrinsic toughening mechanisms in bone tissue(32) as it generates microdamage ahead of the
probe tip. Based on a large dataset of human samples (62 donors), our approach compares
the ability of 1H NMR, RPI, and micro-computed tomography (μCT) (used as a surrogate of
X-ray-based measurements) to explain age-related changes in fracture toughness. In
addition, glycation-mediated, non-enzymatic collagen cross-links were measured because
their accumulation in tissue, together with increased porosity, are thought to impair fracture
toughness.(21)
Material and methods
Bone sample preparation and study design
All cadaveric tissues used in this work were stored fresh-frozen and obtained from the
Musculoskeletal Transplant Foundation (Edison, NJ), the Vanderbilt Donor Program
(Nashville, TN), and the National Disease Research Interchange (Philadelphia, PA). Cortical
bone samples were extracted from the lateral quadrant of the femoral mid-shaft of sixty-two
human donors (30 male donors, aged 21 – 98 years old, mean ± standard deviation: 63.5 ±
23.7 years; and 32 female donors, aged 23 –101 years old, 64.4 ± 21.3 years). Single-edge
notched beam (SENB) specimens (one per donor, N=62) were cut using a circular low-
speed, diamond-embedded saw (660, South Bay Technology, Inc., San Clemente, CA) and
machined using an end mill to a specimen thickness B = 1.9–3.3 mm, width W = 4–6.8 mm,
and length L = 19–31 mm, with B = 0.5.W as per the fracture toughness test standard ASTM
E1820.(33) Micro-notches were created using a low-speed saw and sharpened further into a
pre-crack by means of a razor blade lubricated with 1 μm diamond solution to give original
crack size a0 = 0.9–1.9 mm.
The measuring sequence – schematically described in Fig. 1 – consisted of 1) imaging the
notched region of the specimen with micro-computed tomography (μCT), 2) performing
fracture toughness test until fracture, 3) extracting two bone segments from each SENB
specimen for further analysis by 1H NMR spectroscopy and high performance liquid
chromatography (HPLC), respectively. The specimens were stored in phosphate-buffered
saline (PBS) soaked gauze at −20°C between each phase of the experimental protocol.
Reference point indentation (RPI) was carried out on the surface of the NMR specimens. An
additional measurement campaign was performed to obtain RPI measures closer to a clinical
setting, that is, indenting through the periosteum. The thickness (B) of the SENB specimen
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precluded RPI along the periosteal edge. Instead, RPI was performed on the opposite medial
quadrant (Fig. 1).
Micro-computed tomography analysis (μCT)
Prior to fracture toughness testing, SENB specimens were scanned (μCT50, Scanco
Medical, Switzerland) at an isotropic voxel size of 5 μm using the same settings (tube
voltage: 90 kVp; beam current: 200 μA; 1000 projections per 360° rotation; integration time:
400 ms) and a hydroxyapatite phantom calibration with the manufacturer’s beam hardening
correction.(34) Upon reconstruction, μCT images were post-processed with a Gaussian filter
to suppress image noise (sigma = 1.8 and support of 3). The scanned region was 1.3 mm
wide and encompassed the notch (Fig. 1), allowing for the precise determination of the
original crack size (a0). A volume of interest (VOI) was selected in front of the original
crack tip of each specimen (Fig. 1). Apparent volumetric bone mineral density (avBMD) was
defined as the mean of volumetric bone mineral density for all voxels within the total
volume of the VOI. Tissue mineral density (TMD) was defined as the mean of volumetric
bone mineral density for all voxels assigned to the matrix (voxels with a bone mineral
density more than 660 mgHA/cm3). Intracortical porosity (Ct.Po) was computed as the ratio
of the voxels with a bone mineral density less than 600 mgHA/cm3 per total number of
voxels in the VOI (noise filter set to sigma = 2.0 and support of 2).
In addition, 16 NMR specimens (Fig. 1) were also scanned by μCT and analyzed using the
same settings and parameters as the notch scans to verify two assumptions: 1) the porosity
remains relatively constant throughout the whole SENB specimen, that is Ct.PoNMR and
Ct.Po are similar and 2) porosity determined from 1H NMR highly correlates with porosity
assessed from μCT images.
Fracture toughness testing
Fracture toughness testing procedures adhered to the guidelines of ASTM Standard
E1820.(33) SENB specimens were subjected to three-point bending. The samples were
positioned horizontally on two 1 mm diameter supports with a ~20 mm span S (equal to
4×W)(33) and loaded mid-span (in-line with notch), using an axial servo-hydraulic testing
system (DynaMight 8841, Instron, Norwood, MA), to propagate a crack normal to the
osteonal direction (Fig. 1 and 2A). The force-displacement data was recorded at 50 Hz as the
hydrated bone was tested in displacement control to failure with a progressive, multiple
loading (+0.07 mm at 0.01 mm/s)-unloading (−0.04 mm at 0.015 mm/s)-dwell scheme (Fig.
2B).
Human cortical bone exhibits non-linear mechanical behavior (i.e., a significant amount of
plastic deformation), and as such, its fracture behavior must be studied in the framework of
elastic-plastic fracture mechanics as pointed out by others.(10,35,36) This comes down to
characterizing the so-called rising R-curve, which evaluates the fracture resistance in terms
of the J-integral, as a function of stable crack extension Δa (Fig. 2). Crack lengths (ai) were
computed from the unloading compliance data Ci (Fig. 2B) and specimen geometry by
solving the following equation (Eq A1.10 in ASTM E1820)(33) for each cycle i:
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(1)
where E′=E/(1−ν2), ν is the Poisson’s ratio (taken to equal 0.3), and E is the specimen-
specific flexural Young’s modulus computed as follows:
where δ is the initial stiffness during R-curve testing. We verified that this estimated
modulus matched the modulus derived from eq. 1 for C0 and a0. The value of J was
calculated for each cycle by adding its elastic and plastic components and correcting for
crack growth:
(2)
The detailed equations used to compute the stress-intensity K and the plastic component of J
are provided in ASTM E1820(33) (precisely, Eq. A1.2, A1.3, A1.8 and A1.9). Conditions for
J-dominance were met (i.e. W-a0, B>10(J/σ), where σ is the yield stress) for all 62 fracture
tests. Note that we chose to use a smaller ratio a0/W (0.26±0.03) than the one recommended
by ASTM E1820 to provide more crack events before complete failure occurred. Since
fracture toughness properties depend on this ratio,(37) the absolute values in the present work
tend to be higher than what other studies report for human cortical bone.
Three parameters of interest were retrieved from the R-curve: J-integral and the critical
stress intensities required to initiate cracks (Kinit) and to sustain subsequent crack growth
(Kgrow). Precisely, J-int is the value of J at failure. Crack initiation toughness Kinit was back-
calculated from JIc using the K-J equivalence relationship KJ = (E′.J)1/2 (Fig. 2C). Crack
growth toughness Kgrow was defined as the slope of the linearized plot KJ vs (Δa)1/2 (38)
(Fig. 2D). Kgrow could not be calculated for specimens that exhibit highly brittle behavior,
i.e. when the crack propagation is rapidly unstable and leads to failure immediately after
reaching the peak force. This was the case for 11 samples out of 62.
1H Nuclear magnetic resonance spectroscopy
Recent work has shown that transverse relaxation time constant (T2) from 1H NMR can
separate proton signals from collagen-bound water (~400 μs) and pore water (~1 ms –
1s).(25,26) Along with a microsphere of water as a reference volume (T2 ~ 2 s), bone
specimens were inserted into a custom-built, low proton, loop-gap-style radio-frequency
(RF) coil(26) and placed in a 4.7T horizontal-bore magnet (Varian Medical Systems, Santa
Clara, CA). Upon 90°/180° RF pulses of ~ 5/10 μs duration, Carr-Purcell-Meiboom-Gill
(CPMG) measurements with 10,000 echoes were collected at 100 μs spacing, yielding data
that were fitted with multiple exponential decay functions to generated a T2 spectrum using
the freely available MERA toolbox(39) for MATLAB® (details available in Horch et al.(26)).
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Given the known water volume in the microsphere, the integrated areas of bound water (T2
= 150 μs − 1 ms) and pore water (T2 = 1 ms − 600 ms) were compared to the area of the
reference sample (T2 = 600 ms-10 s) (Fig. 1) and converted into water volumes. Finally,
these volumes were divided by the specimen volume (calculated from Archimedes’
principle) to give bound water (bw) and pore water (pw) volume fractions.
Reference point indentation
The tissue-level mechanical properties of cortical bone were assessed using a BioDent™
instrument (Active Life Scientific, Inc., Santa Barbara, CA).(40–43) The general principle of
RPI consists of measuring the displacement of a stainless steel test probe (375 μm diameter,
90° cono-spherical, 2.5 μm radius tip) that cyclically indents into the bone to a given load
(20 cycles at 2 Hz with a maximum force of 10 N per cycle in this study). There is a short
dwell period (166 ms) between loading and unloading. Throughout RPI testing, the samples
were kept hydrated with PBS. The raw load vs. displacement data was processed using a
custom MATLAB® code(43) to determine a number of indentation resistance properties.
Although a multitude of parameters can be computed from a single acquisition, most of
them are inter-correlated, and therefore provide redundant information.(43) Moreover, the
parameters related to the first cycle typically present higher scatter.(43) Hence, we decided to
retain two parameters commonly reported in RPI studies, namely total indentation increase
(TID) and indentation distance increase (IDI), as well as two parameters known to correlate
with tissue age and toughness, that is the average value from the cycle 3 to 20 of energy
dissipation (avED) and loading slope (avLS).(43)
Ten RPI measurements were collected every 2 mm on the medial quadrant of the midshaft,
below the periosteum (Fig. 1). Five RPI measures were acquired on the surface of the NMR
sample (indentation orthogonal to the osteon direction). Extreme outliers among the
indentations per tested surface for any given RPI property were identified using the
generalized extreme studentized deviate procedure(44) and discarded from further analysis
(1.7% and 3.3% of the measurements on periosteum and NMR samples, respectively) and
the average value per tested surface was computed from the remaining measurements.
High performance liquid chromatography
A small piece of bone (~ 10–50 mg) was cut from the corner of the SENB specimen (Fig. 1).
The sample was first fully demineralized in 20% EDTA (0.68M, pH 7.4), then hydrolyzed
(110°C, 20–24h) in 6N HCl (10μL/mg bone). After removing the acid with a vacuum
concentrator (Savant SPD131DDA SpeedVac with cold-trap; Thermo Scientific; USA), the
hydrosylate was re-suspended in ultrapure water, split (nominal ratio 50:50), and dried in the
vacuum concentrator.
For crosslinking assay, the residue of one split sample was re-suspended in a dissolving
buffer containing an internal standard (1.5 μM pyridoxine). The solution was filtered and
diluted with buffer (0.5% (v/v) heptafluorobutyric acid in 10% (v/v) acetonitrile), and a 50
μL sample was injected into a HPLC system (Beckman-Coulter System Gold 126) fitted
with a silica-based column (Waters Spherisorb®). Varying concentrations of pentosidine
(PE) from the International Maillard Reaction Society combined with a fixed amount of
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pyridoxine were used as standards. PE concentration was calculated from the
chromatograms recorded using a Waters fluorescence detector (328/378 nm excitation/
emission).
PE concentration was then normalized by its respective collagen amount, as determined by a
hydroxyproline assay(45) on the second split sample. Briefly, amino acids and internal
standard (α-amino-butyric acid (α-ABA)) were derivatized with phenyl isothiocyanate
(PITC). The derivatives were re-suspended in a buffer (5% acetonitrile in 5mM disodium
phosphate), along with standards of varying concentrations of hydroxyproline (Hyp) and
proline, and injected into the same HPLC system but with a Pico•Taq® column and a UV
detector. The mole of Hyp per mass of bone calculated from the chromatograms was divided
by 14% (amount of hydroxyproline in type I collagen) and by 0.3 (molecular weight of
collagen in μg/pmol)(46) to give PE concentration as mmol/mol of collagen (PEcoll).
Statistical analysis
A preliminary analysis on a subset of 16 specimens determined whether (i) the porosity at
the notched region (Ct.Po) is equivalent to the porosity of the NMR specimens (Ct.PoNMR),
and (ii) pore water is equivalent to porosity assessed from μCT images (Ct.PoNMR). Because
porosities were not normally distributed, both these comparisons were tested using
Wilcoxon signed rank test and Spearman correlation, respectively.
Because gender did not significantly explain the variance in fracture toughness properties, it
was not included as a covariate in further analysis. As several properties did not follow a
normal distribution (Shapiro-Wilk Test), Spearman correlation coefficients were used to
evaluate the association between fracture toughness properties (Kinit, Kgrow, J-int) and
potential explanatory variables, namely age, apparent volumetric bone mineral density
measured from CT (vBMD), NMR outcomes (bw, pw), and RPI properties (TID, IDI, avED,
avLS) (Table 1). Including age as a covariate, linear regressions on fracture toughness
properties were used to identify parameters that improved the ability of age to explain the
variance in fracture toughness (Table 2). Upon examining inter-correlations between the
explanatory variables with Spearman’s correlation coefficient (Supplemental Table 1), low
correlated parameters (r<0.55) were considered as independent predictors in a backward,
stepwise, multiple regression with the fracture toughness parameters as dependent variables
to determine which combination of parameters/modalities best explain the variance in
fracture toughness properties (i.e., highest adjusted R2, Table 2). Lastly, to explore the
mechanistic origin of fracture toughness, we extended our search for fracture toughness
predictors to non-clinically translatable parameters, namely intra-cortical porosity (measured
via μCT), non-enzymatic collagen cross-links level or AGE content, and tissue mineral
density (Table 3).
The correlations and simple linear regressions were performed using the MATLAB
Statistics Toolbox (The Mathworks Inc., Natick, MA). The general linear models including
the stepwise regression were applied on bootstrapped data (1000 replicates) to account for
the non-normality of most parameters (STATA 12, StataCorp LP, College Station, TX).
Statistical results were considered significant for p-values less than 0.05, unless otherwise
stated.
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Results
Correlations with fracture toughness
All the fracture toughness properties – crack initiation toughness (Kinit), crack growth
toughness (Kgrow), and overall resistance to crack propagation (J-init) – decreased with age
(Table 1). However, the correlation between fracture toughness and age was not particularly
strong with age only explaining 13% to 23% of the variance (Fig. 3A). The NMR-derived
bound water (bw) had the strongest correlation with Kinit and J-init (Table 1). Whereas the 3
fracture toughness properties increased with an increase in bw (Fig. 3B), they decreased with
an increase in NMR-derived pore water (pw), except for Kgrow, which was not significantly
correlated with pw (Fig. 3C). Lower resistance of bone to micro-indentation at the tissue-
level (that is, higher TID, IDI and avED, and lower avLS) was associated with lower Kinit
and J-int at the apparent level (Table 1). The correlation trends were similar whether RPI
was performed through the periosteum (surrogate of clinical setting) or on the surface of the
NMR sample. Apparent volumetric mineral bone density (avBMD) was weakly correlated
with only Kinit.
Multivariate explanation of fracture toughness
Since our potential clinical surrogates (bw, IDI, etc.) did not strongly correlate with fracture
toughness on their own (Table 1 and Fig. 3), we performed a multivariate analysis. When
either NMR properties or RPI properties were combined with age, the explanation of the
variance in Kinit and J-init improved (compare R2 values in Fig. 3 to adjusted R2 values in
Table 2). This improvement was greater than what was obtained combining age and avBMD
(Table 2). With age still as a significant covariate, a combination of NMR-derived properties
(bw and pw) and one RPI property (avLS) provided the best explanation of Kinit (adjusted R2
= 53.3%, p<10−5), whereas a combination of pw and avLS provided the best explanation of
J-int. Only the linear combination of age and avLS (a RPI parameter related to bone matrix
hardness) including a significant interaction (adj-R2 = 23.9%, p=0.001) helped improve the
explanation of the variance in crack growth toughness (Kgrow).
Determinants of fracture toughness
As for the effect of microstructure and matrix properties on fracture toughness, intracortical
porosity, tissue mineral density, and pentosidine levels were all negatively associated with
Kinit and J-int (Table 3). The combination of Ct.Po and PEcoll best explained the variance in
fracture toughness properties (Table 3). Porosity was the only factor that significantly
correlated with crack growth toughness.
To explore why the NMR measurements could be predictive of fracture toughness, we
compared them to the aforementioned determinants. Specifically, we expected bw to be
sensitive to changes in the matrix tissue, i.e. PEcoll and TMD; and pw to reflect differences
in the microstructure (Ct.Po). We found that, similarly to the correlations observed for the
fracture toughness properties, bound water was negatively correlated with pentosidine levels
(r = −0.31, p = 0.014) and TMD (r = −0.42, p = 0.001 for all 62 specimens and r = −0.74, p
= 0.0001 for 14 NMR specimens that were imaged by μCT but excluding 2 highly porous
outliers). The ancillary analysis of the paired 16 specimens showed that porosity was similar
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between NMR specimen and notched region (Wilcoxon signed rank test p-value equal to
0.079 and R2 = 93.7%, Fig. 4A). A direct comparison on the NMR samples showed a high
correlation between pw and Ct.PoNMR (R2 = 66.1% for the 16 specimens, R2 = 93.8% after
removing two outliers, Fig. 4B), confirming that pore water derived from NMR is a valid
measure to assess intracortical porosity. The remarkably high porosities (>35%) of the two
outliers could explain the discrepancies between μCT and NMR since pores at the surface of
the bone specimen are accounted for in the computation of porosity with μCT but not NMR
(Fig. S1). Looking at the whole dataset, the NMR-derived porosity and the porosity assessed
from μCT were also highly correlated (R2 = 56.0%; R2 = 78.1% after removing two outliers,
Fig. 4C).
As for what possibly influences microindentation, we found that the RPI properties did not
correlate with TMD and weakly correlated with PE (periosteal IDI and avgED only), but
surprisingly, some properties were not completely independent of porosity (Supplemental
Table 2) regardless of whether indentation was performed on the periosteal surface or the
NMR specimens.
Discussion
Mounting evidence, both from clinical reports and basic science research, indicate that
fracture risk does not solely depend on strength but also on bone properties at the tissue
level. In this context, there is a growing recognition of the importance of fracture toughness
in assessing fracture risk as it is a measure of the ability of the tissue to resist crack initiation
and propagation.(47) The relative insensitivity of DXA to bone quality motivates the search
for novel clinical surrogates to assess attributes of bone other than bone mass and strength.
For the first time, we show that NMR and to a lesser extent RPI have the potential to assess
fracture toughness properties. Although the techniques presented in our work were applied
to excised samples, both techniques can conceivably be translated to clinical assessment of
fracture risk. Indeed, bound and pore water derived from 1H NMR can be imaged in patients
using selective radiofrequency pulse sequences with ultrashort echo-time magnetic
resonance imaging (UTE-MRI)(28,48) and RPI can be performed directly on the patient’s
tibia.(30,31) Findings from the multivariate analysis call for a multimodal assessment of
fracture risk, thereby reflecting the existence of many contributors to fracture toughness.
Several phenomena occurring at different hierarchical levels of organization underpin the
fracture toughness of bone. In any material, crack growth occurs when the stress near the tip
of a pre-existing flaw reaches a critical value (Kinit) and depends on the ability of the
material to dissipate energy: the more energy-dissipation mechanisms that exist, the more
difficult it is to break a material.(49) In bone, this mainly involves plasticity at the nanoscale
through uncoiling of the collagen molecules and sliding of both mineralized collagen fibrils
and individual collagen fibers.(49–52) Once a crack starts propagating, additional toughening
mechanisms come into play. At the ultrastructural level, bone toughening is achieved by the
development of diffuse microdamage in the tissue surrounding the main crack(53–55) as well
as through mineralized collagen filaments that bridge the surfaces created by the crack
extension.(56–60) While submicron mechanisms govern crack initiation and growth, the latter
is also largely influenced by larger scale toughening mechanisms. For example, cement lines
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can deflect the crack path,(10,14,32,61–63) at least when the crack propagates perpendicular to
the osteons(10,13) at a relatively low strain rate.(64) In addition, an increased porosity induces
larger stress concentrations in the matrix, thereby facilitating crack initiation and growth.
Based upon these observations, non-enzymatic collagen cross-links, which stiffen the matrix
and reduce its energy dissipation,(52,65–67) as well as intracortical porosity have been
naturally proposed as determinant factors of bone fracture toughness.(21) Our findings
support this supposition as porosity and pentosidine levels together explained up to 42% of
the variance in fracture toughness (Table 3).
The aforementioned concepts on what dictates toughening mechanisms helps our
understanding of why NMR-derived properties correlate with fracture toughness of human
cortical bone. Matrix of more porous bone specimens are subjected to higher local stresses
compared to less porous bone and subsequently will not resist as well to the departure of a
crack from the initial notch. Hence, since pore water is directly related to intra-cortical
porosity (Fig. 4) as reported by others,(27,68,69) it is not surprising that increased pore water
was associated with lower crack initiation toughness. The role of the bound water in fracture
toughness likely occurs at the nanoscale in so far as hydration confers plasticity to collagen.
Hence, a less hydrated matrix (i.e. lower bound water) could partially hinder the resistance
to crack initiation and propagation, as observed in our study (Table 1). The negative
correlation between bound water and pentosidine (also found in Nyman et al.(68)) fits with
the observation that chemical cross-linking changes the relaxation time of bound water in rat
tail tendon(70) and causes dehydration of the fibers by drawing the collagen molecules closer
together(71) which could reduce sites of hydrogen bonding between water and collagen.
Similarly, a decrease in bound water was associated with an increase of mineralization
(TMD) also suggesting NMR-derived bw is sensitive to mineral aggregation displacing
collagen-bound water.(72–74) Additional investigations involving manipulation of the bone
matrix are necessary to confirm that the degree of mineralization and non-enzymatic
collagen crosslink concentrations are determinants of bw.
Correlations between NMR-derived bulk properties and fracture toughness were modest
(e.g. r = 0.63 between Kinit and bw, see Table 1), but greater than those between fracture
toughness and avBMD (r < 0.29), suggesting that information about water compartments in
bone (bound and pore water) could be more valuable than a measure of bone mineral density
to assess the ability of bone to resist fracture. These results extend the list of mechanical
properties that are are related to or affected by changes in matrix water(25,75–79) and pore
water.(78) Although moderate, the present correlation coefficients are similar to those found
in other studies between crack initiation toughness (stress intensity factor KIc or critical
strain energy release Gc) and other bone characteristics, e.g. organic weight fraction
(r=0.48),(17) PE (r=−0.48),(8) water content(r=−0.62),(11) microhardness (r=−0.51),(1)
microdamage density (r=−0.40),(19) or porosity (r=−0.61).(16)
Interestingly, NMR properties did not help improve the prediction of Kgrow over age (Table
2) even though porosity is thought to influence both crack initiation and crack growth
toughness. This shed lights on another feature that most likely affects fracture toughness:
matrix heterogeneity. Indeed, based on fracture mechanics theory, a crack will propagate
more readily in a homogeneous material than in a material offering structural/compositional
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interfaces.(80) A loss in tissue heterogeneity has been posited as an explanation for the
occurrence of fractures associated with long-term anti-resorptive treatment, which leads to a
more homogeneous matrix.(4,81–83) Computational studies have also established the
mechanical properties of the cement lines as an attribute likely to affect the propagation of
the crack through bone tissue.(62,84) Hence, by providing only a bulk measurement of bone
tissue and therefore being insensitive to matrix heterogeneity and the microstructural barrier
provided by cement lines, NMR-derived properties may not be suitable to detect changes in
crack growth toughness.
As for RPI, the output properties of this instrument better explained age-related decrease in
fracture toughness than avBMD. Precisely, it is easier for the probe tip to penetrate bone
tissue (i.e. higher indentation distance, higher energy dissipated, lower loading slope) when
bone material exhibits lower fracture toughness. In particular, RPI was the only technique
among the three tested that improved the explanation of crack growth toughness from a
coefficient of determination of 17.1% (age only) to 23.9% (Table 2). While this link
between RPI and crack growth toughness concurs with fracture toughness tests conducted
with compact tension specimens acquired from the tibia mid-shaft of four human donors,(30)
weak correlations between RPI properties and fracture toughness (Table 1) are not entirely
unexpected. The probe tip generates microdamage at the micro-scale, whereas apparent level
fracture toughness depends on a crack propagating over millimeters and can take a tortuous
path. It remains to be seen whether another reference point indenter, namely the
OsteoProbe™, would provide stronger correlations with fracture toughness given that it uses
a large impact load (~45 N) engaging more bone tissue and likely propagating more
microcracks than the cyclic indentation method (10 N) of the BioDent™.
There is a possible limitation to the way bound and pore water were calculated. Indeed, by
normalizing the water measurements by apparent total volume of bone (i.e. including both
the matrix and porous space),(26,27,85) pw and bw are inversely correlated to some extent.
Consequently, for the same level of matrix hydration, a more porous specimen, i.e. with a
lower matrix fraction, exhibits a lower bw. Nonetheless, the partial correlation between age
and bound water when controlling for porosity (pw) remained significant (r = −0.47 with pw
as a covariate, r = −0.52 otherwise), and so the age-related decrease in bound water is not
strongly biased by any concomitant increase in porosity. This was also the case for the
partial correlations between bw and the fracture toughness properties Kinit (r = 0.49), J-int (r
= 0.33), and Kgrow (r = 0.33). Moreover, bw and pw together significantly contributed to
Kinit (Table 2). This was possible to test because of the large size of the cohort with more
than 60 human donors (unusual for a basic science study). Care was taken to balance gender
(32 female, 30 male) and age (from 23 to 98 years old, mean±std = 64±22 y.o.).
In summary, the findings of the present work stress the necessity of a multimodal
assessment of fracture toughness. After investigating the potential of several techniques
sensitive to different features and length scales of bone, an explanation of variance in
fracture toughness can be best achieved by a combination of bound and pore water plus
indentation resistance properties as determined by 1H NMR and RPI, respectively. This
supports the potential of MRI and RPI to complement DXA in clinical assessment of
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fracture risk, especially in diseases likely affecting material properties more so than areal
BMD, e.g. type 2 diabetes.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
The National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health (NIH) under Award Number AR063157 funded this work. The purchase of the BioDent instrument was supported in part by the National Center for Research Resources, Grant UL1 RR024975, and is now at the National Center for Advancing Translational Sciences, Grant UL1 TR000445. The micro-computed tomography scanner was supported by the National Center for Research Resources (1S10RR027631) and matching funds from the Vanderbilt Office of Research. Additional funding to perform the work was received from the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development (1I01BX001018), NIH (1R01EB014308), and the National Science Foundation (1068988).
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Figure 1. Schematic of bone analysis. Upon imaging the notched region of the specimen with micro-
computed tomography (μCT) to determine intra-cortical porosity and volumetric bone
mineral density, each single-edge notched beam (SENB) specimen underwent fracture
toughness testing. After testing, a segment of the SENB specimen (~5 × 5 × 2.5 mm3) was
analyzed by 1H nuclear magnetic resonance (NMR) spectroscopy to determine the fraction
of bound and pore water (i.e., water volume per apparent bone volume). A second bone
segment (~2 × 2 × 2.5 mm3) was analyzed by high performance liquid chromatography
(HPLC) to determine pentosidine, a glycation-mediated, non-enzymatic collagen crosslink.
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Figure 2. Measuring the fracture toughness of human cortical bone using the R-curve method. The
single-edge notched-beam specimen (A) was subjected to a progressive load-unload-reload
scheme (B) with rest insertion before reloading in order to capture images of the crack.
Three representative images after 1, 15, and 23 cycles show the crack propagation during
testing (A). Using a non-linear, elastic fracture mechanics approach, the specimen geometry,
and the slope of unloading curve to estimate crack length (B), the J-integral was computed
as a function stable crack extension (C). Then, crack growth toughness was determined as
the slope of fracture toughness per cycle of loading vs. the square root of crack extension
(D).
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Figure 3. Age-related changes in fracture toughness. Crack initiation toughness, crack growth
toughness, and total energy dissipated during these processes decreased with age (A).
Fracture toughness was directly correlated with bound water (B) and inversely correlated
with pore water (C).
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Figure 4. Strong correlations between μCT-derived porosity and NMR-derived pore water. The
porosity of the NMR specimens correlated with the μCT-derived porosity (5 μm voxel size)
near the notch of the same SENB specimens (A). For this subset of NMR specimens,
porosity strongly correlated with pore water especially when 2 highly porous specimens
were excluded (B). For all specimens excluding the 2 outliers, porosity near the notch
directly correlated with pore water of the SENB portion (C).
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Tab
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om in
dent
atio
ns o
n th
e N
MR
spec
imen
(ita
lics)
rag
eav
BM
Dbw
pwT
IDID
Iav
ED
avL
S
Kin
it (n
=62
)−
0.48
0.29
0.63
−0.
53−
0.44
ns−
0.32
0.24
*
ns−
0.26
−0.
330.
36
Kgr
ow (
n=51
)−
0.34
0.26
*0.
35ns
nsns
nsns
nsns
nsns
J-in
t (n=
62)
−0.
38ns
0.45
−0.
34ns
ns−
0.39
0.44
nsns
ns0.
22*
* p<0.
08
J Bone Miner Res. Author manuscript; available in PMC 2016 July 01.
Author M
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Author M
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Granke et al. Page 22
Tab
le 2
Lin
ear
com
bina
tion
of s
igni
fica
nt p
aram
eter
s ac
ross
mod
aliti
es in
clud
ing
age
of th
e do
nor
for
each
fra
ctur
e to
ughn
ess
prop
erty
(m
odel
s ap
plie
d on
boot
stra
pped
dat
a w
ith 1
000
repl
icat
es)
Fra
ctur
e to
ughn
ess
prop
erty
Exp
lana
tory
var
iabl
esL
inea
r m
odel
Adj
-R2
(%)
Kin
it
age
+ b
one
dens
ity−
0.05
·age
+ 0
.02·
avB
MD
40.8
age
+ N
MR
7.80
− 0
.03·
age
+ 0
.30·
bw −
0.3
4·pw
47.0
age
+ R
PIa
−0.
05·a
ge −
0.0
8·T
ID +
19.
74·a
vLS
37.9
best
com
bina
tion
−0.
03·a
ge +
0.3
0·bw
− 0
.30·
pw +
19.
86·a
vLS
53.3
Kgr
ow
age
+ b
one
dens
ity*
8.32
− 0
.05·
age
17.1
age
+ N
MR
*
age
+ R
PIa
49.7
5 −
0.6
8·ag
e −
88.
04·a
vLS
+ 1
.32·
age·
avL
S23
.9
best
com
bina
tion
23.9
J-in
t
age
+ b
one
dens
ity*
18.6
1 −
0.0
9·ag
e12
.8
age
+ N
MR
22.5
5 −
0.0
7·ag
e −
0.6
2·pw
18.6
age
+ R
PIa
−17
.75
− 0
.09·
age
+ 7
7.9·
avL
S30
.9
Bes
t com
bina
tion
−0.
07·a
ge −
0.4
9·pw
+ 7
1.98
·avL
S35
.2
a Inde
ntat
ions
thro
ugh
the
peri
oste
um
* The
exp
lana
tory
var
iabl
e w
as n
ot s
igni
fica
nt in
the
mul
tivar
iate
mod
el
J Bone Miner Res. Author manuscript; available in PMC 2016 July 01.
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Granke et al. Page 23
Tab
le 3
Mec
hani
stic
ori
gin
of c
hang
es in
fra
ctur
e to
ughn
ess.
Spe
arm
an c
orre
latio
n co
effi
cien
ts (
r) a
re in
dica
ted
whe
n si
gnif
ican
t at p
<0.
05. L
inea
r m
ultiv
aria
te
anal
ysis
was
app
lied
on b
oots
trap
ped
data
with
100
0 re
plic
ates
.
Spea
rman
cor
rela
tion
coe
ffic
ient
Mul
tiva
riat
e an
alys
is B
est
com
bina
tion
*A
dj-R
2 (%
)C
t.P
oP
Eco
llT
MD
Kin
it−
0.51
−0.
34−
0.28
12.2
4 −
0.1
9·C
t.P
o −
0.0
003·
PE
coll
42.2
Kgr
ow−
0.27
**ns
nsns
ns
J-in
t−
0.23
**−
0.32
−0.
3218
.61
− 0
.25·
Ct.
Po
− 0
.007
· PE
coll
21.1
* age
was
not
incl
uded
as
a co
vari
ate
in th
e m
ultil
inea
r m
odel
bec
ause
of
the
stro
ng c
orre
latio
n be
twee
n ag
e an
d PE
coll
(r =
0.6
5)
**p<
0.07
J Bone Miner Res. Author manuscript; available in PMC 2016 July 01.