P.-H Blard
email: sylvie.cappelle@chu-brugmann.be

Discriminating value of HR-pQCT for fractures in women with similar FRAX scores: a 1 substudy of the FRISBEE cohort 2 3

Keywords: osteoporosis, fracture prediction, HR-pQCT, FRISBEE, FRAX, UDR aBMD 57 58

Osteoporosis (OP) is a condition characterized by loss of bone mass and bone micro-architectural 64 deteriorations leading to an increased bone fragility and occurrence of fractures [START_REF]Consensus development conference: Diagnosis, prophylaxis, and treatment of osteoporosis[END_REF]. The mechanical properties

65

of bone tissue and the osseous resistance depend on bone mass and bone quality -a complex notion which refers 66 to a combination of micro-architecture, accumulated microscopic damage, collagen maturity and biomechanical 67 modifications, the degree of mineralization, mineral crystal size, non-collagenous proteins and bone turnover [START_REF] Reid | Overview of Pathogenesis[END_REF].

68

Both bone mass and quality deteriorate with ageing. With increasing life expectancy, the growing size of the 69 ageing population raises the burden of osteoporotic fractures (OFs) and makes it necessary to develop cost-70 effective screening techniques to identify individuals at high risk of fracture in order to provide them an early 71 and appropriate treatment.

73

The operational diagnosis of OP is based on the measurement of areal bone mineral density (aBMD) by 74 dual-energy X-ray absorptiometry (DXA) and defined by a T-score value more than or equal to 2.5 standard 75 deviations (SD) below the young healthy adult mean at lumbar spine, femoral neck or total hip [START_REF]Consensus development conference: Diagnosis, prophylaxis, and treatment of osteoporosis[END_REF]. This classical 76 definition does not allow an optimal risk prediction since more than half of OFs occur in postmenopausal women 77 classified as osteopenic or with normal BMD [START_REF] Who | Assessment of Fracture Risk and Its Application to Screening for Postmenopausal 421[END_REF]. The risk ascribable to bone density in subjects with a fragility 78 fracture is lower than 50% [START_REF] Kanis | on behalf of the World Health Organisation Scientific Group, Assessment of 423 osteoporosis at the primary healthcare level[END_REF]. A better prediction of OFs should also take into account factors reflecting bone 79 architecture and biomechanical alterations. These are in part determined by several clinical risk factors (CRFs), 80 such as age, low BMI, corticosteroid therapy, a prior fragility fracture or a parental hip fracture history, which 81 are associated with fracture risk independently of BMD [START_REF] Kanis | Diagnosis of osteoporosis and assessment of fracture risk[END_REF].

83

These CRFs, alone or in association with BMD, have been combined to construct different models to calculate 84

OFs probabilities at 5 and 10 years [START_REF] Kanis | Diagnosis of osteoporosis and assessment of fracture risk[END_REF]. The most widely used is the FRAX score, which is based on a set of ten

85

CRFs with or without BMD measurement and provides a 10-year probability to sustain a major osteoporotic 86 fracture (MOF: forearm, shoulder, clinical spine or hip fracture) or a hip fracture. This user-friendly online 87 algorithm has largely proven its ability to improve the fracture prediction with regards to BMD alone, resulting 88 in a better selection of patients requiring a treatment on the basis of a threshold fracture risk, for instance a 10 89 year risk above 20% for MOFs [START_REF] Jiang | Diagnostic accuracy of FRAX in predicting the 10-year risk of osteoporotic fractures 432 using the USA treatment thresholds: A systematic review and meta-analysis[END_REF].

90

Besides, some micro-architectural parameters not included in aBMD measured by DXA could be associated with

95

HR-pQCT also allow a micro-finite element analysis (µFEA) of bone strength [START_REF] Krug | High-Resolution Imaging Techniques for the 435 Assessment of Osteoporosis[END_REF].

96

Multiple cross-sectional and several prospective studies have shown an association of some HR-pQCT 97 parameters with the presence of prevalent fractures or the risk of incident fractures in men and women, persisting

98

after adjustment for CRFs or hip aBMD [START_REF] Nishiyama | Clinical Imaging of Bone Microarchitecture with HR-pQCT[END_REF][START_REF] Mikolajewicz | HR-pQCT Measures of Bone Microarchitecture Predict 442 Fracture: Systematic Review and Meta-Analysis[END_REF][11].

100

The Fracture RISk Brussels Epidemiological Enquiry (FRISBEE) study is a large population-based 101 cohort study evaluating risk factors for osteoporotic fractures in post-menopausal Belgian women [START_REF] Cappelle | Distribution of clinical risk factors 454 for fracture in a Brussels cohort of postmenopausal women: The FRISBEE study and 455 comparison with other major cohort studies[END_REF]. We used 102 this cohort to evaluate in an ancillary study the impact of HR-pQCT parameters on fracture risk.

Study population and methods

111

This study is a case-control study nested in the FRISBEE project that has been described elsewhere [START_REF] Cappelle | Distribution of clinical risk factors 454 for fracture in a Brussels cohort of postmenopausal women: The FRISBEE study and 455 comparison with other major cohort studies[END_REF].

112

Briefly, 3560 postmenopausal women, aged between 60 and 85 years, living in the Brussels' area were recruited participate. Reasons for non-participation of the 69 women with OFs were: refusal to take part at the 126 investigation for medical reasons or age [START_REF] Levine | Post Hoc Power Analysis: An Idea Whose Time Has Passed?[END_REF], death [START_REF] Mikolajewicz | HR-pQCT Measures of Bone Microarchitecture Predict 442 Fracture: Systematic Review and Meta-Analysis[END_REF], lost to follow-up [START_REF] Krug | High-Resolution Imaging Techniques for the 435 Assessment of Osteoporosis[END_REF], recent investigation by their GP,

127

remoteness or missed appointments [START_REF] Burghardt | Reproducibility of direct 480 quantitative measures of cortical bone microarchitecture of the distal radius and tibia by HR-481 pQCT[END_REF]. For each fracture case who accepted to participate, two controls, 128 matched for their baseline FRAX score, were randomly selected among the sub-group of 1740 women who were 129 similarly reevaluated (HR-pQCT, DXA, CRFs). In order to obtain unbiased estimates of relative risk, controls 130 have been selected by incidence density sampling, using the macro developed by Richardson [13]. This method At baseline, all women answered a standardized questionnaire that has been previously described [START_REF] Cappelle | Distribution of clinical risk factors 454 for fracture in a Brussels cohort of postmenopausal women: The FRISBEE study and 455 comparison with other major cohort studies[END_REF].

148

The

212

For the 417 patients followed up after HRpQCT, relative risk estimates with their 95% CI were obtained using

213

Cox model, the endpoint being the first fracture occurring after the HR-pQCT assessment. Each parameter was 214 transformed in SD units in order to express outcomes as ORs or HRs per 1 SD diminution. To gain a better parts a principal component analysis (PCA) for the radius and the tibia separately after standardization of the

232

The main sites of the 225 fractures (including multiple fractures) were the wrist (25.7%), the spine (16.0%), the

Discussion

299

In this Belgian study, we investigated the predictive value of HR-pQCT for osteoporotic fractures.

300

FRISBEE participants who sustained an incident fracture during the first five years of follow-up were compared

301

to women with similar initial FRAX score. To our knowledge, it is the first study where participants were 302 matched for their FRAX score at inclusion. This approach allows to examine whether HR-pQCT adds 303 information to bone fragility with regards to FRAX with BMD. Moreover, in the second part of the study, all 304 participants were monitored during an average follow-up of 3.6 yrs for the occurrence of fragility fractures.

306

In this cohort, only 35 out of the 188 women who sustained a fracture (19%) had a 10-year 307 probability of MOF ≥ 20 % at baseline so that more than 80% of these would not have been taken into account

308

for treatment considering this fixed threshold (76% would not have been treated if BMD alone had been taken into account). In countries where the threshold for therapy depends on age, these proportions are likely to vary, 310 but a majority of patients would still not be treated. Sornay-Rendu and colleagues found in the OFELY study 311 that approximately half of the women who sustained a fragility fracture were not identified as high risk by the

312

FRAX score [START_REF] Sornay-Rendu | The FRAX tool in French women: 495 How well does it describe the real incidence of fracture in the OFELY cohort[END_REF]. The same was true for the Manitoba cohort-based study or the Lausanne University Hospital 313 study, reflecting that FRAX (and the different defined thresholds) is not always an optimal predictive criteria, as 314 it is the case for BMD [START_REF] Leslie | Independent Clinical 498 Validation of a Canadian FRAX Tool: Fracture Prediction and Model Calibration[END_REF][START_REF] Aubry-Rozier | What was your fracture risk 501 evaluated by FRAX® the day before your osteoporotic fracture?[END_REF]. Additionally, almost 40% of the women who sustained a fracture (74/188) had an 315 estimated fracture probability lower than 10% (low risk range). These figures and those from other retrospective

316

or prospective cohorts suggest that the FRAX, just as BMD, underestimates the risk of fragility fractures [START_REF] Jiang | Diagnostic accuracy of FRAX in predicting the 10-year risk of osteoporotic fractures 432 using the USA treatment thresholds: A systematic review and meta-analysis[END_REF].

317

This observation is partially explained by the fact that the score does not consider some CRFs that might have an 318 impact on fracture risk. History of falls and physical activity for example could be important [START_REF] Gregg | Physical Activity, Falls, and Fractures Among Older 504 Adults: A Review of the Epidemiologic Evidence[END_REF].

320

Bone strength first depends on bone density: this important factor explains, depending on the studies,

321

between 60 and 80% of the variability of bone strength. However, the bone quality, a concept that describes 322 aspects of bone composition and structure, also contributes to bone strength. There is thus room for possible 323 improvement of fracture prediction, using additional quantitative parameters reflecting the bone structure and/or 324 strength. Several studies suggest that HR-pQCT might outperform DXA in fracture risk prediction [START_REF] Mikolajewicz | HR-pQCT Measures of Bone Microarchitecture Predict 442 Fracture: Systematic Review and Meta-Analysis[END_REF].

325

Our nested case-control analysis showed that several structural and/or biomechanical parameters characterized 326 those who suffered an incident fracture. This highlights the importance of the structural information. Greater 327 impairments of the trabecular parameters (Tb.vBMD, TbN, Tb.Sp.SD) were observed at the radius of the 328 fractured women while only cortical density, porosity and thickness were statistically associated with fractures at 329 the tibia. The small number of hip fractures present in our sample may impact these results. However, such 330 discrepancies according to the site of measurement were previously noted in other cross-sectional studies 331 comparing HR-pQCT parameters in women with and without prevalent fractures [START_REF] Boutroy | 508 Microarchitecture and Peripheral BMD are Impaired in Postmenopausal White Women With 509 Fracture Independently of Total Hip T -Score: An International Multicenter Study[END_REF][START_REF] Litwic | Bone 512 Phenotype Assessed by HRpQCT and Associations with Fracture Risk in the GLOW Study, 513[END_REF]. It may be explained by 332 the broader cortical thickness of the tibia and by the fact that the tibia, in contrast to the radius, is a weight-333 bearing bone and is hence exposed to other mechanical stimuli that differently impact the architectural 334 parameters. This difference is also observed in FEA results: a majority of the bone resistance parameters were 335 significantly associated with fractures at the radius, while only the apparent modulus differed significantly 336 between the two groups at the tibia.

338

In the second part of the study, fewer parameters were predictive for fractures. They were mainly 339 density parameters (Tt.vBMD, Ct.vBMD, Tb.vBMD) and app. modulus at the radius and only D.cort at the tibia.

No association remained significant after adjustment for the FRAX score, which was significantly higher in the 341 group of patients who sustained a fracture after the HR-pQCT. This can result from the valuable predictive 342 power of the FRAX tool. However this observation could also arise from an insufficient statistical power 343 resulting from a too small number of incident fractures. The 46 fractures observed during the prospective study 344 were indeed far below the total number of events needed considering the ORs of the retrospective part (i.e. 119 345 to 484 depending on the considered HR-pQCT parameter; http://sample-size.net/sample-size-survival-analysis 346 [START_REF] Levine | Post Hoc Power Analysis: An Idea Whose Time Has Passed?[END_REF]). Nevertheless, the data of this subsequent analysis confirm our first conclusions, with both trabecular and 347 cortical densities lower at the radius when only the cortical density identifies those who will fracture at the tibia, 348 and the HRs of the follow up analysis are significantly correlated with the ORs of the nested case study (data not 349 shown). Moreover, the hazard ratios observed in our study are concordant with those of the large international 350 cohort recently described by Samelson et al, where the risk was increased by around 10 to 60% for 1 SD 351 decrease for each considered parameter [11] (Fig. 2). In that study, HRs were still significant after adjustment for 352 the FRAX.

354

We did not find any association of cortical porosity at the radius or the tibia with fractures. The association 355 between this parameter and the occurrence of OFs is indeed quite variable in the literature depending on the 356 method used to define the zone of interest for the evaluation. For example, Kral and colleagues using a new more 357 inclusive algorithm (non-threshold-based software) found a correlation between the cortical porosity and the 358 prevalence of non vertebral fractures, independent of the FRAX score [START_REF] Kral | Increased cortical 518 porosity and reduced cortical thickness of the proximal femur are associated with nonvertebral 519 fracture independent of Fracture Risk Assessment Tool and Garvan estimates in postmenopausal 520 women[END_REF]. In contrast and unexpectedly, in the

359

GLOW study, history of fracture was associated with lower cortical porosities [START_REF] Litwic | Bone 512 Phenotype Assessed by HRpQCT and Associations with Fracture Risk in the GLOW Study, 513[END_REF]. The synthesis of the various 360 prospective studies published by Samelson et al did not show any significant association between cortical 361 porosity and fracture risk [11]. The average great age of our cohort (~75 yrs at HR-pQCT assessment) could also 362 be an explanation: the fact that the majority of bone loss occurs in the cortical compartment after the age of 65 363 years could have led to bias toward null differences between our groups of participants [START_REF] Zebaze | Intracortical remodelling and porosity in the distal radius and post-mortem 523 femurs of women: a cross-sectional study[END_REF].

365

Additionally, our results do not indicate that failure load is an independent predictor for fracture (HR 1.22 [0.94-366 1.58]). This is at odds with the BoMIC results [11]. Apart from an insufficient power due to the low rate of 367 fracture events in our cohort, this discrepancy could be explained by different methods for failure load 368 calculation, hampering adequate comparison of our results. In the BoMIC consortium, the used FEA conditions 369 are different from one cohort to another. Authors harmonized the data using a modulus of 6.829 GPa (while we 370 considered two modulus of 17 and 20 GPa for trabecular and cortical bone, respectively). Compared to the BoMIC methodology, this leads to an increase of the Failure Load in our cohort [START_REF] Whittier | Harmonizing finite element 526 modelling for non-invasive strength estimation by high-resolution peripheral quantitative 527 computed tomography[END_REF] and therefore to a lower 372 risk. When our results are compared to those of the OFELY and STRAMBO studies (2 cohorts included in the 373 BoMIC), that used the same approach than in Frisbee, the HRs were similar (HR 1.44 [1.21-1.72] and 1.79 374 [1.44-2.23], respectively [START_REF] Boutroy | 508 Microarchitecture and Peripheral BMD are Impaired in Postmenopausal White Women With 509 Fracture Independently of Total Hip T -Score: An International Multicenter Study[END_REF][START_REF] Szulc | Prediction of Fractures in Men Using Bone 530 Microarchitectural Parameters Assessed by High-Resolution Peripheral Quantitative Computed 531 Tomography-The Prospective STRAMBO Study[END_REF]) 375 376 Furthermore, considering that, for economic reasons, the assessment of the bone structure and/or 377 strength by HR-pQCT will not replace the systematic screening of fracture risk by DXA and/or FRAX in a 378 nearby future, we examined if specific groups could benefit from this method. Considering the subgroup of our 379 HR-pQCT cohort having a FRAX score < 20% (low and intermediate risk): we found in the second analysis that 380 the trabecular thickness of the radius discriminated participants with incident fractures (p=0.05, data not shown).

381

However, the small number of fractures in this ancillary study precludes drawing definitive conclusions.

383

Finally, it is of interest that UDR aBMD provides a lot of information. Significant risk gradient was 384 associated with this parameter both in the nested case and follow up part of the study, with a risk increase of 385 almost 66% per SD decrease. The fact that almost all HR-pQCT ORs were no longer significant after adjustment 386 for UDR aBMD supports this conclusion. This results from the strong correlation between the HR-pQCT 387 parameters and the UDR aBMD. Additionally, UDR aBMD is the only variable that is still significant after 388 adjusting for the FRAX score in the follow-up study, keeping in mind that the statistical power of the 389 prospective part is limited. In their prospective study, Biver et al also demonstrated a very good performance of 390 the UDR aBMD for fracture prediction (HR ~1.75) [START_REF] Biver | Evaluation of 535 Radius Microstructure and Areal Bone Mineral Density Improves Fracture Prediction in 536 Postmenopausal Women[END_REF]. This good predictive power is probably explained by 391 the fact that UDR aBDM combines both trabecular and cortical bone and is thus a pertinent composite predictor 392 of bone strength.

393

The strengths of our nested case-control study are that all fragility fractures were prospectively assessed 394 and X-ray verified, all CRFs were collected at baseline and confirmed each year by phone and at the time of HR-395 pQCT measurements. A restriction of this analysis is the short delay between baseline and the second evaluation 396 (5.2 ± 0.9 yrs), while the FRAX predictions are based on a 10 year risk evaluation. Nevertheless, the design of 397 our study guarantees a similar follow-up duration for the Fx and the non-Fx groups. It is also relevant to note 398 that only 10% of the controls (30/298) had a subsequent fracture during the second period, so during a total 399 period of 8.8 ± 1.1 yrs. In addition to the small number of fractures, the relatively short follow-up (median 3.6 400 yrs) of the prospective part of the study is another weakness because, which can explain that some of the 401 measured parameters were not predictive of fractures.

In conclusion, although HR-pQCT cannot be considered as a screening tool for now, it may be of 403 interest in some subgroups. It is especially of major interest in the understanding of the biomechanical process of 404 fracture as well as the action of anti-osteoporotic treatments. Our results highlight the superiority of the radius 405 measurements in postmenopausal women and confirm the interest of the UDR aBMD that contains major 406 information on fracture risk. As follow-up is ongoing, a further prospective analysis of incident fractures is

  Discriminating value of HR-pQCT for fractures in women with similar FRAX scores: A substudy of the FRISBEE cohort Sylvie Isabelle Cappelle, Michel Moreaux, R. Karmali, L. Iconaru, F. Baleanu, V. Kinnard, Marianne Paesmans, S. Rozenberg, M. Rubinstein, M. Surquin, et al. To cite this version: Sylvie Isabelle Cappelle, Michel Moreaux, R. Karmali, L. Iconaru, F. Baleanu, et al.. Discriminating value of HR-pQCT for fractures in women with similar FRAX scores: A substudy of the FRISBEE cohort. BONE, 2021, 143, pp.115613. 10.1016/j.bone.2020.115613. hal-03047095

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  fracture risk, independently of aBMD. High-resolution peripheral quantitative computed tomography (HR-92 pQCT) has been developed to get insights into bone micro-architecture. It provides a quantitative 3D assessment 93 of the volumetric BMD (vBMD) for the cortical and trabecular compartments. Owing to a high resolution, it 94 allows the evaluation of cortical porosity and indices of trabecular micro-architecture. The data obtained with

113between119

  July 2007 and June 2013. Their CRFs for OP or fracture were recorded at baseline and are reevaluated 114 each year by telephone contact. All women underwent a spine and hip aBMD measurement at inclusion. 115 Fractures occurring during follow-up were registered and systematically validated by radiological To ensure a minimum follow-up period of three years, only the first 1740 women included in the 120 FRISBEE cohort before July 2010 were considered in the present study (Fig. 1). In July 2013, 188 of them had 121 sustained at least one fragility fracture (for a total of 225 fractures as some participants presented multiple 122 fractures). Between August 2013 and September 2014, these 188 women were re-invited for a quantitative and 123 qualitative assessment of their bone architecture by HR-pQCT, for a reevaluation of their CRFs and the 124 realization of a second DXA, including a radius aBMD measurement. One hundred and nineteen accepted to 125

  199 variables: the percentage of load carried by the trabecular and cortical bone at the distal and proximal surface of 200 the volume of interest (Tb.Dist.Load, Tb.Prox.Load, Ct.Dist.Load and Ct.Prox.Load, %, respectively), the 201 average and SD values of the Von Mises stresses in the trabecular and cortical bone (Tb.Av.Stress and 202 Ct.Av.Stress; Tb.SD.stress and Ct.SD.stress, MPa, respectively), the stiffness (kN/mm), the apparent modulus 203 and the estimated failure load (N). t-tests were used to compare densities, microarchitecture, and mechanical parameters of207women in the 2 groups. For the nested case control analysis, odds ratio estimates (OR) with their 95% CI were 208 obtained using conditional logistic regressions which consider subject matching. Because of the method of 209 controls selection (without replacement from all persons at risk at the time of case occurrence), the sample used 210 is representative of the full cohort and OR should be interpreted as hazard ratios (HR)[START_REF] Langholz | Case-Control Studies = Odds Ratios: Blame the Retrospective Model[END_REF]. Univariate and 211 bivariate (adjustment for OP treatment at the time of HR-pQCT) analysis were conducted.

217variables. 1

 1 Very simply, PCA transforms the original interrelated variables into a new set of uncorrelated 218 variables called Principal Components (PCs) (e.g. [24]). PCs are linear combinations of the original variables 219 which represent most of the variance of the dataset. Each PC can be interpreted on the basis of the weight of the 220 initial variables. Finally, as these PCs are uncorrelated, they were used in multivariate analysis as predictor of 221 fracture. In the nested case control part, a multivariate conditional logistic analysis was performed using a 222 backward selection model with a p-value cut-off of 0.05. In the second part, we used a Cox model and a 223 backward selection of the PCs with a p-value cut-off of 0.05. All significance probabilities are for two-sided 224 tested and we set the threshold for significance at p<0.05. First part (fractures which occurred before HR-pQCT)

Figure 1 -Figure 2 -

 12 Figure 1 -Time line and follow-up of the cohort -Study design

2 Clinical risk factor data collection -questionnaire and clinical assessments 147

	131
	132	involves matching each fractured woman (case) to a sample of those with similar risk who had not already
	133	fractured at the time of case occurrence (selection without replacement). In other words, women with a fracture
	134	could be selected as control, as long as they had not fractured. For example, a woman, included in the study in
	135	September 2007, who fractured in August 2009 (time to fracture = 2 years) can be considered as a control for
	136	any other participant who fractured less than 2 years after her inclusion in FRISBEE (whatever the date of this
	137	inclusion). This was the case of 24 participants. As HR-pQCT measurements were performed in more subjects
	138	than were necessary for defining the control group, a total of 417 evaluations were available. All these 417
	139	participants were followed up and fractures that occurred after the HR-pQCT assessment were registered with
	140	the endpoint being the first fracture occurring after the HR-pQCT assessment. This design allowed a hindsight of
	141	5.2 ± 0.9 yrs between the inclusion and the realization of the HR-pQCT and a follow-up of 3.6 ± 0.7 yrs after the
	142	HR-pQCT (September 2018 evaluation).
	143
	144	All procedures were approved by the CHU-Brugmann Hospital ethical committee and participant consent was
	145	obtained before study initiation (approval number B07720072493).
	146
		2.

Table 3

 3 Thirty radius and eight tibiae scans had to be excluded because of poor quality. The percentage of rejected scans 249 did not differ significantly between the fracture and control groups.According to the study design, women with incident fractures after inclusion in FRISBEE did not differ from 251 FRAX-matched controls for the main CRFs included in the FRAX. As expected, FN aBMD was similar in the 252 two groups. On the contrary, ultra distal radial aBMD measured at inclusion in the sub-study was significantly 253 lower in the fractured group (Fx) (Table2, left part). shows the follow-up results of the 417 women included in this sub-study (37 radius and 11 tibiae scans 270 excluded because of poor quality, no difference of percentage of rejected scans between the two groups). No At the tibia, Ct.vBMD was the only parameter significantly associated with fracture risk. After 279 adjustment for treatment or FRAX recalculated at inclusion into the sub-study, none of the HR-pQCT parameters 280 were still significantly predictive but UDR aBMD remained significantly associated with fracture risk.In the nested case-control study PCA, four principal components explaining at least 10% of the total variance 284 emerged for the radius and the tibia. Together they explained 91% and 86% of the total variability of our whole 285 set of bone characteristics parameters. For the follow-up, we found three (for the radius) and four (for the tibia)286principal components explaining at least 10% of the total variance and explaining 82% and 86% of the total 287 variability at the radius and the tibia, respectively. The correlations of each parameter with the PCs are presented 288 in Table4, where the highest correlations are marked in bold. These correlations indicate the degree and 289 direction of each of the original variables' contribution to each component.

	238
	239
	240	3.1.2	Second part (fractures which occurred after HR-pQCT)
	241	Forty six subjects (30 initially control women and 16 already fractured women) fractured after the HR-pQCT
	242	evaluation. Thirty nine of these fractures were defined as MOFs (6 hips, 9 shoulders, 10 wrists, 14 vertebrae)
	243	according to the FRAX definition and six as "other major" fractures. New incident fractures occurred 2.2 ± 1.1
	244	yrs [18 days -4.4 yrs] after the second evaluation date (Table 1).
	245
	246	3.2 BMD and HR-pQCT
	247

233

shoulder (15.1%), the ankle (10.7%) and the hip (9.0%). Out of the 188 considered fractures, 127 were defined 234 as MOFs according to the FRAX model, 34 as "other major" fractures and 27 as minor fractures. Out of the 119 235 fractures of the HR-pQCT group, 71 were defined as MOFs (8 hips, 16 shoulders, 36 wrists, 11 vertebrae), 23 as 236 ''other major'' fractures and 25 as minor fractures. The mean time to first fracture was 2.7 ± 1.7 yrs [5 days -6.2 237 yrs]. All fracture types and numbers are summarized in Table 1.

258

Concerning biomechanical parameters: at the radius, both trabecular and cortical SD stresses were higher 259 (p=0.02) in Fx vs non-Fx. Cortical SD stresses were also higher at the tibia (p<0.01). Apparent modulus and 260 estimated failure load were significantly lower at the distal radius (p=0.001 and 0.05) and tibia (p<0.01 and 261 0.03). Women with fractures had lower stiffness at the tibia (p=0.01).

262

At the radius, each SD decrease of Tt.vBMD, Tb.vBMD, Tb.N, Tb.Sp.SD, Trab and Cort.SD.Stress, stiffness, 263 app. modulus and failure load values was significantly associated with fractures (p<0.05). The association 264 remained significant after adjusting for treatment, except for Tb.N. At the distal tibia, each SD decrease of the 265 value of Ct.vBMD, Ct.Th and app. modulus was significantly associated with fractures even after adjusting for 266 treatment (p<0.05) (Table

2

. right part). apparent modulus between Fx and non-Fx women (p=0.01).

276

In this second part, a decrease of aBMD, Tt.vBMD, Ct.vBMD, Tb.vBMD or app. modulus at the radius was 277 associated with a significant increase of fracture risk (p < 0.05), even after adjusting for treatment (except for Tb.vBMD).

290

In both PCAs, the composition of the first PC was quite similar at the radius and the tibia, regrouping the total 291 density (and aBMD for the radius) and several qualitative parameters (cortical and trabecular SD stresses, 292 cortical average stress, stiffness, apparent modulus and failure load). Only the first PC of the radius was 293 significantly associated with an increased fracture risk (HR 1.16 [1.04-1.28], p <0.01). This PC explained by 294 itself ~50% of the total variance for the radius. Again, the tibia was less predictive.

Table 1 :

 1 Fractures distribution for Major Osteoporotic Fractures (MOF) according to FRAX and other fractures,

	593
	594	mean time to first fracture
	595
	596
		Fractures	First part -before HR-pQCT		Second part -after HR-pQCT
				Total women	With multiple fractures	Total fractures	Reevaluated women	With multiple fractures	Total women	Initial case women	Initial control women
			Total	188	37	225	119	20	46	16	30
			Hip	17	2	19	8	0	6	3	3
		MOFs FRAX	Shoulder	29	5	34	16	3	9	3	6
			Wrist	53	5	58	36	5	10	5	5
			Vertebrae	28	8	36	11	3	14	3	11
			Pelvis	7	4	11	5	2	4	1	3
		Other major
		fractures	Ankle	19	5	24	11	3	1	1	0
			Elbow	3	1	4	3	1	1	0	1
			Femur	0	1	1	0	0	0	0	0
			Tibia	1	2	3	1	1	0	0	0
			Humerus	1	0	1	1	0	0	0	0
			Sacrum	3	0	3	2	0	0	0	0
			Carpus	3	0	3	3	0	0	0	0
		Non major
		fractures	Clavicle	1	0	1	1	0	0	0	0
			Ribs	4	2	6	4	0	0	0	0
			Metatarsus	12	2	14	11	2	0	0	0
			Metacarpus	0	0	0	0	0	1	0	1
			Patella	2	0	2	2	0	0	0	0
			Scapula	1	0	1	0	0	0	0	0
			Tarsus	4	0	4	4	0	0	0	0
		MOFs FRAX	%	67.6	54.1	65.3	59.7	55.0	84.8	87.5	83.3
		All major	%	85.6	89.2	86.2	79.0	90.0	97.8	100	96.7
		Mean time to first fracture Days	956±591 [5-2269]	-	-	973±619 [5-2269]	-	803±419 [18-1594]	817±482 [18-1594]	795±391 [51-1438]
	597
	598

Table 2 :

 2 HR-pQCT parameters of the women who fractured between inclusion and HR-QCT (Fx) and their

	599
	600	matched controls (C) (mean ± SD, OR [range])
			Fx (n=119)	C (n=238)	p	OR per ↓ 1 SD	OR adjusted for treatment	OR adjusted for UDRaBMD
		Initial FRAX	12,6±6.7	12,6±6.7	NS	-	-	-
		Age at baseline (yr)	70.7±6.3	69.9±6.2	NS	-	-	-
		Age at HR-pQCT (yr)	76.5±6.3	75.2±6.1	NS	-	-	-
		FN aBMD at baseline (g/cm²)	0.703±0.09	0.693±0.11	NS	-	-	-
		FN aBMD at HR-pQCT (g/cm²)	0.668±0.09	0.667±0.09	NS	0.98 [0.71-1.35]	0.93 [0.66-1.30]	-
		OP medication use	12.6%	21.8%.	0.02
		Radius
		aBMD UDR mg/cm²	0.348±0.07	0.365±0.06	0.02	1.67 [1.22-2.28]**	1.63 [1.19-2.23]**	-
		Tt.vBMD mg/cm³	263±58	280±69	0.02	1.41 [1.07-1.86]*	1.39 [1.05-1.84]*	1.66 [0.99-2.80]
		Ct.vBMD mg/cm³	822±78	832±87	0.30	1.16 [0.89-1.50]	1.15 [0.88-1.49]	1.02 [0.70-1.49]
		Tb.vBMD mg/cm³	126±39	138±44	0.01	1.45 [1.10-1.90]**	1.43 [1.08-1.89]*	1.29 [0.87-1.92]
		Ct.Th µm	597±185	626±207	0.19	1.20 [0.92-1.57]	1.18 [0.90-1.55]	1.08 [0.70-1.68]
		Ct.Po	69±6	68±6	0.13	0.83 [0.65-1.07]	0.84 [0.65-1.08]	0.76 [0.55-1.05]
		Tb.N* mm-1	1.64±0.42	1.72±0.39	0.07	1.32 [1.01-1.72]*	1.29 [0.98-1.70]	1.05 [0.74-1.49]
		Tb.Th µm	64±12	66±13	0.25	1.23 [0.94-1.62]	1.21 [0.92-1.60]	1.28 [0.88-1.87]
		Tb.Sp µm	599±262	554±234	0.11	0.78 [0.59-1.03]	0.78 [0.59-1.04]	1.00 [0.69-1.44]
		Tb.Sp SD µm	365±296	298±236	0.05	0.73 [0.54-0.99]*	0.74 [0.55-0.99]*	0.90 [0.62-1.31]
		Trab.Prox.Load %	49±9	50±10	0.45	1.13 [0.87-1.47]	1.12 [0.86-1.46]	1.07 [0.78-1.47]
		Trab.Dist.Load %	16.7±8.7	17.5±8.2	0.39	1.18 [0.91-1.53]	1.17 [0.90-1.51]	1.08 [0.81-1.46]
		Trab.Av.Stress Mpa	8.2±1.7	8.1±1.7	0.72	0.90 [0.69-1.18]	0.92 [0.70-1.20]	1.15 [0.80-1.65]
		Trab.SD.Stress Mpa	5.4±1.4	5.0±1.4	0.02	0.66 [0.49-0.89]**	0.67 [0.50-0.90]**	0.74 [0.47-1.17]
		Cort.Av.Stress Mpa	16.4±3.9	15.9±3.8	0.21	0.91 [0.83-1.01]	0.92 [0.83-1.01]	0.98 [0.83-1.15]
		Cort.SD.Stress Mpa	4.3±1.3	4.0±1.2	0.02	0.67 [0.50-0.88]**	0.68 [0.51-0.90]**	0.67 [0.44-1.02]
		Stiffness kN/mm	116±28	122±27	0.06	1.55 [1.12-2.15]**	1.53 [1.10-2.14]*	1.41 [0.79-2.51]
		App.Modulus	2802±631	3062±765	0.001	1.67 [1.24-2.25]***	1.65 [1.23-2.23]**	2.41 [1.43-4.08]**
		Est.Failure.Load N	2774±665	2919±634	0.05	1.60 [1.14-2.23]**	1.58 [1.13-2.21]**	1.44 [0.82-2.25]
		Tibia
		Tt.vBMD mg/cm³	227±50	237±48	0.07	1.21 [0.94-1.55]	1.20 [0.93-1.54]	1.23 [0.85-1.79]
		Ct.vBMD mg/cm³	761±86	785±84	0.01	1.32 [1.03-1.70]*	1.33 [1.04-1.71]*	1.28 [0.94-1.73]
		Tb.vBMD mg/cm³	144±34	146±36	0.59	1.04 [0.82-1.32]	1.01 [0.79-1.31]	0.91 [0.66-1.26]
		Ct.Th µm	713±261	785±272	0.01	1.29 [1.01-1.63]*	1.29 [1.01-1.64]*	1.30 [0.96-1.77]
		Ct.Po	105±16	103±8	0.04	0.79 [0.62-1.02]	0.79 [0.62-1.02]	0.66 [0.46-0.95]*
		Tb.N* mm-1	1.7±0.4	1.7±0.4	0.36	0.90 [0.72-1.14]	0.88 [0.69-1.12]	0.82 [0.61-1.09]
		Tb.Th µm	71±13	74±15	0.07	1.16 [0.92-1.46]	1.15 [0.91-1.46]	1.09 [0.82-1.46]
		Tb.Sp µm	559±217	558±182	0.98	0.99 [0.78-1.25]	1.01 [0.79-1.29]	1.01 [0.75-1.34]
		Tb.Sp SD µm	307±257	300±208	0.78	0.96 [0.77-1.21]	0.98 [0.78-1.24]	0.96 [0.73-1.28]
		Trab.Prox.Load %	57±9.5	56±10.2	0.34	0.95 [0.76-1.18]	0.94 [0.75-1.17]	0.87 [0.68-1.12]
		Trab.Dist.Load %	35±9.7	34±10.7	0.53	0.91 [0.73-1.11]	0.89 [0.71-1.12]	0.86 [0.66-1.11]
		Trab.Av.Stress Mpa	4.0±1.3	3.9±1.1	0.28	0.91 [0.73-1.13]	0.92 [0.74-1.14]	0.96 [0.75-1.23]
		Trab.SD.Stress Mpa	2.2±0.8	2.1±0.7	0.06	0.86 [0.68-1.08]	0.86 [0.68-1.09]	0.95 [0.72-1.25]
		Cort.Av.Stress Mpa	7.3±2.5	6.9±2.0	0.18	0.89 [0.72-1.13]	0.91 [0.72-1.14]	0.98 [0.74-1.28]
		Cort.SD.Stress Mpa	1.7±0.7	1.5±0.5	<0.01	0.79 [0.62-1.01]	0.79 [0.62-1.01]	0.84 [0.63-1.12]
		Stiffness kN/mm	289±58	305±55	0.01	1.27 [0.98-1.66]	1.26 [0.97-1.65]	0.97 [0.68-1.39]

Table 3 :

 3 HR-pQCT parameters of the women who fractured after HR-QCT (Fx) and controls (non-Fx) (mean ±

	602
	603	SD, HR [range])
			Fx (n=46)	Non-Fx (n=371)	p	HR per ↓ 1 SD	Adjusted for treatment	Adjusted for FRAX
		FRAX at baseline	16.1±10.2	11.4±6.4	<0.01	-
		FRAX at HR-pQCT	19.3±12.2	14.3±7.1	<0.01	-
		Age at HR-pQCT	76.9±5.7	74.7±6.0	0.01	-
		OP medication use	30.4%	15.6%	<0.01	-
		FN aBMD 1 (g/cm²)	0.676±0.11	0.709±0.10	0.05	-
		FN aBMD 2 (g/cm²)	0.641±0.09	0.677±0.09	0.01	1.39 [0.98-1.98]	1.32 [0.92-1.91]	1.13 [0.75-1.71]
		Radius
		aBMD UDR mg/cm²	0.336±0.07	0.365±0.06	<0.01	1.66 [1.18-2.35]**	1.63 [1.14-2.32]**	1.47 [1.02-2.13]*
		Tt.vBMD mg/cm³	254±68	280±66	0.02	1.48 [1.08-2.03]*	1.41 [1.02-1.95]*	1.25 [0.90-1.75]
		Ct.vBMD mg/cm³	808±99	836±78	0.09	1.40 [1.04-1.87]*	1.35 [1.00-1.81]*	1.14 [0.82-1.59]
		Tb.vBMD mg/cm³	123±41	137±42	0.04	1.37 [1.01-1.85]*	1.29 [0.95-1.76]	1.24 [0.92-1.66]
		Ct.Th µm	574±212	631±194	0.08	1.36 [0.99-1.88]	1.30 [0.94-1.80]	1.10 [0.77-1.56]
		Ct.Po	70±7	68±6	0.06	1.19 [0.84-1.69]	1.16 [0.82-1.65]	1.12 [0.79-1.59]
		Tb.N* mm-1	1.6±0.5	1.7±0.4	0.14	1.27 [0.95-1.68]	1.20 [0.89-1.61]	1.13 [0.86-1.49]
		Tb.Th µm	63±13	66±13	0.12	1.26 [0.91-1.75]	1.20 [0.86-1.67]	1.20 [0.87-1.64]
		Tb.Sp µm	597±259	554±223	0.26	0.84 [0.66-1.08]	0.87 [0.67-1.13]	0.92 [0.71-1.20]
		Tb.Sp SD µm	354±271	304±245	0.33	0.81 [0.62-1.05]	0.83 [0.63-1.09]	0.90 [0.69-1.17]
		Trab.Dist.Load %	49±9	49±10	0.74	1.18 [0.77-1.44]	1.03 [0.76-1.39]	1.15 [0.85-1.55]
		Trab.Prox.Load %	16±8	17±8	0.48	1.11 [0.80-1.53]	1.07 [0.78-1.48]	1.18 [0.88-1.59]
		Trab.Av.Stress Mpa	8.3±1.6	8±1.8	0.45	0.89 [0.65-1.22]	0.93 [0.68-1.27]	1.05 [0.78-1.43]
		Trab.SD.Stress Mpa	5.5±1.5	5±1.4	0.05	0.77 [0.58-1.02]	0.81 [0.58-1.016]	0.88 [0.67-1.17]
		Cort.Av.Stress Mpa	17±4	16±4	0.12	0.93 [0.84-1.03]	0.95 [0.85-1.05]	0.97 [0.838-1.08]
		Cort.SD.Stress Mpa	4.4±1.4	4±1.2	0.08	0.79 [0.60-1.04]	0.83 [0.63-1.10]	0.90 [0.68-1.21]
		Stiffness kN/mm²	114±28	120±27	0.13	1.30 [0.92-1.85]	1.22 [0.85-1.74]	1.09 [0.77-1.55]
		App.Modulus	2740±720	3032±732	0.01	1.49 [1.07-2.05]*	1.42 [1.02-1.97]*	1.30 [0.92-1.84]
		Est.Failure.Load N	2736±634	2882±638	0.17	1.27 [0.90-1.80]	1.18 [0.83-1.69]	1.07 [0.76-1.52]
		Tibia
		Tt.vBMD mg/cm³	22 8±60	239±47	0.24	1.28 [0.95-1.74]	1.28 [0.948-1.742]	1.06 [0.76-1.47]
		Ct.vBMD mg/cm³	757±97	784±78	0.08	1.34 [1.02-1.76]*	1.31 [0.99-1.72]	1.14 [0.84-1.55]
		Tb.vBMD mg/cm³	142±36	148±34	0.28	1.20 [0.89-1.63]	1.10 [0.81-1.51]	1.08 [0.79-1.47]
		Ct.Th µm	741±337	792±258	0.34	1.21 [0.90-1.63]	1.18 [0.87-1.59]	1.00 [0.72-1.39]
		Ct.Po	104±9	103±11	0.32	0.90 [0.73-1.11]	0.89 [0.72-1.09]	0.90 [0.71-1.15]
		Tb.N* mm-1	1.7±0.4	1.7±0.4	0.94	0.96 [0.71-1.31]	0.90 [0.66-1.22]	0.88 [0.65-1.19]
		Tb.Th µm	71±13	74±13	0.13	1.33 [0.97-1.82]	1.29 [0.94-1.77]	1.31 [0.95-1.80]
		Tb.Sp µm	548±168	553±186	0.85	1.04 [0.74-1.45]	1.14 [0.80-1.63]	1.16 [0.81-1.67]
		Tb.Sp SD µm	290±156	296±221	0.82	1.02 [0.73-1.44]	1.12 [0.78-1.60]	1.15 [0.78-1.69]
		TrabDistLoad %	57±10	56±10	0.81	0.99 [0.73-1.32]	0.96 [0.72-1.28]	1.07 [0.80-1.44]
		TrabProxLoad %	35±12	34±10	0.47	0.89 [0.67-1.21]	0.88 [0.66-1.18]	0.99 [0.74-1.33]
		TrabAvStress Mpa	4±1.7	3.9±1.3	0.69	0.96 [0.74-1.26]	1.00 [0.76-1.31]	1.03 [0.76-1.39]
		TTrabSDStress Mpa	2.2±0.9	2.1±0.8	0.25	0.86 [0.67-1.10]	0.89 [0.69-1.15]	0.91 [0.69-1.19]
		CortAvStress Mpa	7.3±2.7	7±2.4	0.44	0.90 [0.70-1.17]	0.94 [0.72-1.22]	0.96 [0.72-1.27]
		CortSDStress Mpa	1.7±0.7	1.6±0.6	0.15	0.83 [0.65-1.06]	0.85 [0.66-1.10]	0.90 [0.68-1.18]
		Tstiffness kN/mm²	297±62	304±55	0.45	1.15 [0.85-1.55]	1.08 [0.78-1.46]	0.97 [0.69-1.37]

understanding of the interrelationship among highly correlated HR-pQCT parameters, we also conducted in both

the study was supported by IRIS-Recherche, CHU Brugmann and the GIPOB. We sincerely thank all 24 the members of the INSERM Research Unit 1033 of Lyon and more particularly Stéphanie Boutroy for her precious

619

 417 women of the FRISBEE cohort, matched for FRAX, were evaluated by HR-pQCT 620  Some HR-pQCT parameters, mainly at radius, are discriminating for prevalent fractures

621

 HR-pQCT improves the risk prediction beyond the DXA but not the FRAX score

622

 UDR aBMD also provides valuable information