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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

Granke et al. Page 2

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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

le 1

Cor

rela

tion

betw

een

frac

ture

toug

hnes

s pr

oper

ties

and

prop

osed

exp

lana

tory

var

iabl

es (

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rman

cor

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tion

coef

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ents

(r)

are

indi

cate

d w

hen

sign

ific

ant a

t p<

0.05

). F

or R

PI, p

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s fr

om in

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ns th

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e pe

rios

teum

are

list

ed a

bove

the

prop

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s fr

om in

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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

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n=51

)−

0.34

0.26

*0.

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nsns

nsns

nsns

nsns

J-in

t (n=

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0.45

−0.

34ns

ns−

0.39

0.44

nsns

ns0.

22*

* p<0.

08


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Tab

le 2

Lin

ear

com

bina

tion

of s

igni

fica

nt p

aram

eter

s ac

ross

mod

aliti

es in

clud

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age

of th

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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


Author M

anuscriptA

uthor Manuscript

Author M

anuscriptA

uthor Manuscript


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


hhs public access 1,2 1,2,3,4 sasidhar uppuganti1,2 mark d … · 2016-11-11 · identifying novel...

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