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. 2023 Aug 29;18(8):e0290812. doi: 10.1371/journal.pone.0290812

Association of trabecular bone score and bone mineral apparent density with the severity of bone fragility in children and adolescents with osteogenesis imperfecta: A cross-sectional study

Yasuhisa Ohata 1,#, Taichi Kitaoka 1,#, Takeshi Ishimi 1, Chieko Yamada 1, Yukako Nakano 1, Kenichi Yamamoto 1,2,¤, Shinji Takeyari 1, Hirofumi Nakayama 1,3, Makoto Fujiwara 1, Takuo Kubota 1, Keiichi Ozono 1,*
Editor: Heather Macdonald4
PMCID: PMC10464990  PMID: 37643181

Abstract

Osteogenesis imperfecta (OI) is a hereditary skeletal disease characterized by bone fragility. Areal bone mineral density (BMD), evaluated by dual-energy X-ray absorptiometry (DXA), is used to assess bone brittleness. The height-adjusted BMD Z-score (BMDHAZ) is calculated in children and adolescents with OI to reduce the confounding factor of short stature. However, even with the BMDHAZ, severity evaluation in children and adolescents with OI is challenging because certain abnormalities in bone quality cannot be accurately assessed by BMD analysis. The trabecular bone scores (TBS) and bone mineral apparent density (BMAD), which represent the structural integrity of bone and bone-size-associated BMD, respectively, are associated with fracture risk. Recently, age- and sex-specific reference ranges have been reported, enabling the calculation of Z-scores for children. To evaluate which density measurements show the highest correlation with fracture risk, we analyzed the associations between the Z-scores of TBS, BMAD, and BMDHAZ, fracture rate, and genetic variants. We retrospectively reviewed 42 participants with OI aged 5 to 20 years who underwent DXA. COL1A1/2 pathogenic variants were detected in 41 of the 42 participants. In participants with nonsense and frameshift variants (n = 17) resulting in haploinsufficiency and mild phenotype, the TBS Z-score was negatively correlated with fracture rate (FR) (r = -0.50, p = 0.042). In participants with glycine substitution (n = 9) causing the severe phenotype, the BMAD Z-scores were negatively correlated with FR (r = -0.74, p = 0.022). No correlation between the BMDHAZ and FR was observed in both groups. These findings suggest that the TBS and BMAD are useful in assessing children and adolescents with OI with specific genetic variants.

Introduction

Osteogenesis imperfecta (OI) is a rare inheritable skeletal disorder, caused, in most cases, by structural or quantitative defects in the α1 and α2 chains of collagen type Ⅰ, encoded by COL1A1 or COL1A2 genes, respectively [1, 2]. These patients show bone fragility, deformities of long bones, and short stature. The bone brittleness results in fractures even after minor trauma and subsequent growth restriction [3]. The clinical classification of OI was first established by Sillence and colleagues in 1979 [4] and five clinical forms are defined in the revised “Nosology and Classification of Genetic Skeletal Disorders.” [5] OI type Ⅰ has the mildest phenotype while type Ⅳ is a moderately severe form. Among the non-lethal forms of OI, type Ⅲ patients are most severely affected. It has been reported that a quantitative deficiency in collagen type Ⅰ causes a mild form of OI [6], while a structural defect derived from glycine substitutions in COL1A1 or COL1A2 genes results in a more severe form [7].

It is very important to evaluate the severity of bone fragility in children and adolescents with OI to make a diagnosis and provide appropriate treatment and lifestyle management advice. Evaluation of areal bone mineral density (BMD) using dual-energy x-ray absorptiometry (DXA) can aid OI diagnosis [8–10], given that OI patients have significantly lower BMD compared to healthy individuals of the same age and sex [11, 12]. However, bone density measurement in growing bone is challenging due to the inherent complexities of measuring the projection density of objects of varying sizes. Moreover, the thickness of the bone itself affects the projected density. Bone density is strongly related to height; thus, short stature may also contribute to low BMD [13]. As a result, The International Society for Clinical Densitometry has recommended a height-adjustment approach for DXA measurements of lumbar spine in short stature children [14] using the height-for-age Z-score (HAZ)-adjusted spine areal BMD-for-age Z-score (BMDHAZ) [13]. Bone mineral apparent density (BMAD) calculation, in which bone area is transformed to bone volume of each vertebra to estimate the effects of bone depth and bone size, is also endorsed for clinical evaluation of skeletal features [14, 15]. Previously, two studies evaluated BMD in children and adolescents with OI using different parameters. Rauch et al. studied the relationship between genotype and lumbar spinal areal BMD Z-score with adjustment for age, sex, and height Z-scores [16], while, Diacinti et al. examined the mean BMAD before and after intravenous neridronate therapy [17]. Although neither study assessed bone fragility, BMD Z-scores, and BMAD may be useful approaches to evaluate bone fragility in children and adolescents with OI.

Other skeletal features, including bone microarchitecture are also associated with bone strength and risk of fracture. These aspects of the bone are known as bone quality [18], which can be assessed by bone biopsies and high-resolution peripheral quantitative computed tomography (HR-pQCT) [19, 20]. Indeed, bone quality is impaired not only in animal models of OI [21, 22] but also in OI human bones [23–25]. Furthermore, microscopic mechanical properties are lower in bones of children with OI [26] and negatively correlate with disease severity [27, 28]. To estimate the bone fragility, it is necessary to perform a combined evaluation of bone strength (measured by bone density) and bone quality (reflected by the bone structure and material properties), especially in bones with underlying molecular defects that are associated with altered material properties, such as in patients with OI. While the aforementioned findings provide the rationale for performing bone biopsy routinely in OI assessment of children and adolescents, this approach is difficult to implement given the invasiveness of bone biopsy and the specialized expertise required for its widespread use. Furthermore, HR-pQCT has much higher costs and more limited accessibility than that of DXA [29–31].

Trabecular bone score (TBS) is a gray level texture index extracted from a DXA image of the lumbar spine which evaluates pixel gray level variation and serves as an indirect index of trabecular architecture [32]. While TBS is an indirect analysis of microarchitecture in trabecular bone, it is supposed to estimate fracture risk based on the correlation with connectivity density, trabecular number, and trabecular separation [33–35]. Actually, it has been reported that TBS is lower in severe adult OI patients [36]. Although with a small sample size, Rehberg et al. analyzed TBS in children with OI and suggested TBS to be a useful tool for monitoring the skeletal changes [37]. These findings possibly imply that TBS can complement standard density measures in assessing skeletal integrity in children and adolescents with OI. Nevertheless, TBS Z-score was not previously available for children and adolescents due to the lack of reference data. Recently, however, reliable age- and sex-specific reference ranges of TBS and BMAD have been reported [38, 39], enabling the calculation of the Z-score for individuals aged from 5 to 20 years. Fraga et al. evaluated the Z-score of TBS for healthy Brazilian children and adolescents [40]. Additionally, the BMAD Z-score has been used for cancer survivors [41] and arthrogryposis patients [42] to assess bone health conditions in children and adolescent populations. However, the relationship between these scores and bone fragility were not evaluated in these studies, and no publication has yet assessed these Z-scores in children and adolescents with OI. To evaluate the clinical applicability of TBS and BMAD as an assessment of bone fragility in children and adolescents with OI, we analyzed the associations between the Z-scores of TBS, BMAD, and BMDHAZ, fracture rate, and genetic variants in this cross-sectional study.

Materials and methods

Study population

This cross-sectional study was approved by the Institutional Review Board (IRB) of Osaka University (IRB number 688, 15601, and 19535). Written informed consent was obtained from participants aged 16 years or older. For participants under 16 years of age, parental written informed consent was obtained. Additionally, consent from individuals over 8 years of age was also obtained in written form. All procedures for this study followed the ethical standards of the institution and the 1964 Helsinki declaration. Between 2015 to 2022, a total of 44 participants who had received a genetic diagnosis of OI underwent DXA at Osaka University, ranging in age from 5 to 20 years. Among them, two participants were excluded from enrollment: one due to the unavailability of a written informed consent form, and another because of the inability to conduct an accurate DXA scan following lumbar spine surgery for scoliosis correction. Thus, overall, 42 participants with OI harboring pathogenic variants were included in this study. Participants with lumbar fractures were excluded from the study since they did not undergo DXA. If DXA was performed several times in an individual participant during this observational period, we selected the latest data and analyzed them (N = 39). One of the authors (T.Ki., T.Ku., or K.O.) assessed each participant and assigned classifications according to the Sillence classification. All individuals reported here were Asian ethnicity resided in Japan.

Genetic analysis

As we previously reported [43], we performed targeted next-generation sequencing (NGS) and whole exome sequencing (WES). All of the candidate pathogenic variants detected by targeted NGS and WES were confirmed by Sanger sequencing using a 3730 DNA analyzer (Thermo Fisher Scientific, Waltham, MA, USA). If genetic analysis had not been performed, but a pathogenic variant was identified in an affected family member, the detected variant was considered to be the causative variant for the participant.

Anthropometric measurements

When the participants underwent DXA, their body weight (kg) and height (cm) were measured at our hospital using a digital electronic scale and stadiometer, respectively, and the body mass index (BMI) was calculated. If the participants could not stand correctly, we measured their recumbent height using an inelastic tape. HAZ and Z-scores of body weight and BMI were calculated based on reference data from The Japanese Society for Pediatric Endocrinology [44].

Fracture assessment

Each participant or their parents reported their clinical history of bone fractures at every DXA scan, including vertebral and non-vertebral fractures confirmed radiographically. To verify this information, we also reviewed the participants’ medical charts and counted the number of fractures retrospectively. To calculate the annual fracture rate, we divided the number of fractures up to the DXA measurement by the age at the DXA scan.

DXA assessment

Areal BMD (g/cm2) at the lumbar spine (L1-L4) of all participants was measured by a Hologic Discovery A DXA scanner (from 2015 to 2020) and a Hologic Horizon A DXA scanner (from 2021 to 2022). We further confirmed cross-calibration between these DXA machines. DXA data were analyzed using version 13.5 software (Hologic Inc., Bedford, MA, USA). All measurements were analyzed for bone mineral content, bone area, and aBMD at the department of radiology of our hospital by a professional technician. BMDHAZ was evaluated using the data from previous studies [45–47]. Spine BMAD was calculated as previously reported [39], Z-score was determined using the reference and LMS values [39]. To evaluate TBS, the existing data were collected and analyzed by TBS iNsight software (ver 3.03, Medimaps, Plan-les Ouates, Switzerland). The Z-score of TBS was calculated using the reference and LMS values by sex and age [38].

Statistical analysis

All statistical analyses were performed with JMP® Pro software version 15.0.0 (SAS institute Inc., Cary, NC, USA). The Shapiro-Wilk test was used to determine the distribution of continuous data. Normally distributed variables were expressed as mean ± standard deviation (SD). Non-normally distributed variables were expressed as median (interquartile range [IQR]). To determine the association between two variables, Pearson correlation was used for normal distribution data, while Spearman rank correlation was used for non-normally distributed data. The Wilcoxon signed-rank test was used to compare the difference in non-normally distributed continuous variables between the two groups. The difference with p value < 0.05 was considered statistically significant.

Results

Characteristics of study participants

In this study, 42 participants with a genetically confirmed clinical diagnosis of OI from 40 unrelated families were enrolled. A total of 22 males and 20 females were included. The median age of the participants at the DXA measurement was 13.6 years (range: 4.99–20.4). The distribution of the Sillence classification was as follows: type Ⅰ (n = 31); type Ⅲ (n = 5); type Ⅳ (n = 5); type Ⅴ (n = 1). Genetic analysis was performed all participants and the distribution of detected variants among 42 individuals was as follows: COL1A1 (n = 32), COL1A2 (n = 9), and IFITM5 (n = 1) genes. At the time of DXA analysis, 14 participants had no treatment for OI, while 24 received bisphosphonate (7 treated with risedronate, 9 treated with pamidronate, 4 treated with alendronate, and 4 treated with zoledronic acid) and 4 received active vitamin D analogue (eldecalcitol) (Table 1). Among 14 individuals without any treatment at the DXA scan, 13 had been treated previously (5 received risedronate and pamidronate, 5 received pamidronate and alendronate, 1 received pamidronate, 1 received alendronate, and 1 received alendronate and active vitamin D analogue [alfacalcidol]). Detailed data of the study participants are presented in S1 Table.

Table 1. Characteristics of the study population.

OI participants
Sex, Male/Female 22/20
Age at DXA (years) 13.6 [10.4, 18.9] (4.99, 20.4)
Sillence classification Ⅰ, 31; Ⅲ, 5; Ⅳ, 5; Ⅴ, 1
Genetic analysis COL1A1, 32
COL1A2, 9
IFITM5, 1
Tx at DXA measurement (tx, number of participants) no treatment, 14
risedronate, 7
pamidronate, 9
alendronate, 4
zoledronic acid, 4
eldecalcitol, 4
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Data are presented as mean ± SD or median [interquartile range] and (range). DXA, dual-energy x-ray absorptiometry; OI, osteogenesis imperfecta; Tx, treatment.

Assessment of bone fragility by Z-scores of BMDHAZ, BMAD, and TBS

The annual fracture rate was considered as an index of bone fragility. We analyzed the relationship of the fracture rate with the Z-scores of BMDHAZ, BMAD, and TBS to determine which factors are associated with OI fracture risk. When we analyzed all participants, there was no correlation between the fracture rate and Z-scores of BMDHAZ (ρ = -0.0031, p = 0.98), BMAD (ρ = -0.093, p = 0.56), and TBS (ρ = -0.19, p = 0.22) (Fig 1).

Fig 1. Association of bone fragility with DXA parameters in all OI participants.

Fig 1

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Correlation between annual fracture rate (FR) and Z-scores of TBS, BMAD, and BMDHAZ in OI participants (n = 42) by Spearman rank.

Forty one participants had pathogenic variants in COL1A1 or COL1A2 genes. Nonsense and frameshift variants in one COL1A1 allele result in haploinsufficiency and mild phenotype. On the other hand, the glycine substitution variants, either in COL1A1 or COL1A2 genes, cause severe OI [6, 7, 43]. In this study, 17 individuals had nonsense (n = 6) and frameshift (n = 11) variants in COL1A1 gene while 9 had glycine substitution variant either in COL1A1 (n = 4) or COL1A2 (n = 5) genes (Table 2). To evaluate non-severe subset of OI, we analyzed only individuals with haploinsufficient variants (n = 17) and found that their TBS Z-score was negatively correlated with annual fracture rate (r = -0.50, p = 0.042) (Fig 2). In addition, after excluding participants with Sillence type Ⅲ to extract non-severe participants, the TBS Z-score was still negatively correlated with annual fracture rate (r = -0.38, p = 0.022, S1 Fig). On the other hand, in glycine substitution group, annual fracture rate was negatively correlated with the Z-score of BMAD (r = -0.74, p = 0.022) (Fig 3).

Table 2. Characteristics of the study population with haploinsufficiency and glycine substitution variant in COL1A1 and COL1A2 genes.

Haploinsufficiency glycine substitution
Sex, Male/Female 9/8 4/5
Age at DXA (years) 13.8 ± 5.23 11.5 ± 3.77
Sillence classification Ⅰ, 17 Ⅰ, 2; Ⅲ, 3; Ⅳ, 4
Genetic analysis COL1A1, 17 COL1A1, 4
COL1A2, 5
Height Z-score -0.82 ± 0.89 -3.63 ± 2.26
Body weight Z-score -0.49 ± 1.37 -2.34 ± 2.24
BMI Z-score -0.04 ± 1.12 -0.08 ± 0.92
Annual fracture rate (incidence/year) 0.33 ± 0.20 0.74 ± 0.50
BMDHAZ Z-score 0.13 ± 1.34 1.27 ± 1.85
BMAD Z-score 0.21 ± 1.37 0.12 ± 1.83
TBS Z-score 0.27 ± 1.10 0.28 ± 1.02
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Data are presented as mean ± SD. DXA, dual-energy x-ray absorptiometry; BMI, body mass index, BMDHAZ Z-score, bone mineral density-for-age Z-score adjusted for height-for-age Z-score; BMAD, bone mineral apparent density; TBS, trabecular bone score.

Fig 2. Association of bone fragility with DXA parameters in haploinsufficient defect group.

Fig 2

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Correlation between annual fracture rate (FR) and Z-scores of TBS, BMAD, and BMDHAZ in OI participants with COL1A1 haploinsufficient variants (n = 17) by Pearson correlation.

Fig 3. Association of bone fragility with DXA parameters in glycine substitution group.

Fig 3

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Correlation between annual fracture rate (FR) and Z-scores of TBS, BMAD, and BMDHAZ in OI participants with glycine substitution variants in COL1A1 or COL1A2 (n = 9) by Pearson correlation.

Discussion

For the first time in existing literature, our study determined the correlations between Z-scores of TBS, BMAD, and BMDHAZ and fracture risk in children and adolescents with OI. Interestingly, the TBS Z-score was associated with the fracture rate only in genetically stratified non-severe OI participants. When we extracted clinically non-severe subset by excluding individuals with Sillence type Ⅲ, the TBS Z-score was also negatively correlated with annual fracture rate. (S1 Fig). Through pQCT evaluation, it was observed that areas of high and low BMD were interspersed within the same bone in children and adolescents with mild OI [23]. Although BMD by a phantom-based measurement is almost uniformly low, direct assessments of bone using quantitative backscattered electron imaging or ash of bone revealed the presence of patchy mineral increases in OI bone [1]. TBS is estimated by analyzing the variogram of the projected image of the region of interest in which the sum of the squares of the difference in gray-level between pixels at the determined distance are calculated [29]. From these fundamental mechanisms, it is postulated that TBS can assess the trabecular microarchitecture. In fact, several ex vivo studies showed correlations between TBS and connectivity density, trabecular number, and trabecular separation [33–35]. If a “patchy” abnormality of mineralization in mild OI bones, which contributes to the bone fragility, is detectable by TBS, our result would be of clinical significance. However, the mechanism by which the haploinsufficient variants contribute to the observed abnormalities in OI bones needs to be elucidated. Furthermore, it is essential to clarify whether the dosage of bisphosphonate can impact the correlation between TBS and fracture rate, as patients with more severe bone fragility often receive intensified treatment regimens. Further investigations with HR-pQCT or pQCT may provide valuable insights into the underlying pathophysiology of OI.

Although the mechanism by which the glycine substitution in COL1A1 and COL1A2 genes result in short stature remained incompletely understood [48], we and other groups have reported that OI patients with glycine substitution are distinguishable not only by bone fragility but also by short stature [43, 49]. Consistent with previous reports, individuals with glycine substitution had a greater annual fracture rate and were significantly shorter than those with haploinsufficient variants in this study (S2 Fig). The BMAD and BMDHAZ are calculated by considering bone size and short stature, respectively. Our findings revealed that BMAD correlated with bone fragility only in severe OI, suggesting that an abnormality of bone size, which is corrected by BMAD measurements, can cause inaccuracies in the classical assessment of BMD for these participants. Conversely, when the BMAD is applied to adjust for the smallness of bones, the measurement of BMD by DXA can effectively evaluate bone fragility including bone quality in severe cases of OI. Although the Z-score of BMAD and BMDHAZ were correlated with each other in this study in all participants as well as in the glycine substitution group (S3 Fig), only BMAD Z-score correlated with fracture rate. This finding may suggest that the formula used in BMAD calculation is more appropriate to correct for the bone size effect in severe OI of children and adolescents. Furthermore, it suggests that considering a measurement of BMAD that accounts for bone size leads to a stronger correlation with fracture rates.

This study has some limitations in its design. Although we calculated the Z-scores of TBS and BMAD based on the reference of non-African Americans [38, 39], there was no information on how many Asian participants were included in the reference data. Second, the TBS reference data published by Kalkwarf et al. was based on TBS iNsight software pre-release version 4.0. In this study, we analyzed the TBS by version 3.03 as we were unable to use the pre-release model; additionally, we could not evaluate the difference between these different models. Third, Rehberg et al reported that there was no significant difference of TBS between before and after bisphosphonate treatment in children with OI (p = 0.25) [37]. However, ideally, this study should have been carried out in bisphosphonate-naïve patients, as it is well-known that bisphosphonate can positively affect the BMD in the bones of patients with OI [50]. Unfortunately, we could not evaluate the effects of any OI treatments, including bisphosphonate, because of the retrospective cross-sectional nature of the study, and many participants had already received OI treatments at the time of the investigation (S1 Table). Therefore, in the future, it will be necessary to conduct a longitudinal study to analyze the impact of treatments on our findings. Fourth, the sample size was small, particularly in the subgroup analysis, because of the rarity of OI. However, we confirmed that the subgroup used in our study was statistically appropriate through statistical review and that our study has the potential to serve as a pilot study for future, larger, multi-center studies. Finally, we analyzed the fracture rate as a marker of severity of bone fragility in participants with OI aged 5 to 20 years. The risk of fractures can vary depending on the age of patients, as patient activity level, growth rate, and the stage of puberty are completely different between early childhood and adolescence. We need to analyze more patients with OI in a narrow age range to rule out such bias.

In summary, the current study revealed that the Z-score of TBS can evaluate the bone fragility in mild children and adolescents with OI, while that of BMAD can assess bone fragility in severe OI. These findings suggested the calculation of Z-scores of TBS and BMAD is useful for the assessment of children and adolescents with OI. Furthermore, appropriate assessment method must be selected based on the genetic variants of OI patients.

Supporting information

S1 Table. Detailed data of the study participants.

Age, age at the DXA scan; FR, the number of fractures up to the DXA scan divided by the age; BMDHAZ, height-for-age Z-score-adjusted bone mineral density-for-age Z-score; BMAD, Z-score of bone mineral apparent density; TBS, Z-score of trabecular bone score; Ht-SD, standard deviation score of height; BW-SD, standard deviation score of body weight; Tx, treatment at the DXA scan; past Tx, past treatment history; RIS, risedronate, PAM, pamidronate; ALN, alendronate; ZOL, zoledronic acid; Elde, eldecalcitol; Alfa, alfacalcidol; MSCT, mesenchymal stem cell transplantation in utero; none, no treatment; n.d., not detected; † We previously confirmed this deletion variant causing exon 21 skipping (p.Gly364_Arg399del) by mRNA analysis (Takeyari S, Kubota T, Ohata Y, Fujiwara M, Kitaoka T, Taga Y, et al. 4-Phenylbutyric acid enhances the mineralization of osteogenesis imperfecta iPSC-derived osteoblasts. J Biol Chem. 2021;296:100027. Epub 20201123. doi: 10.1074/jbc.RA120.014709. PubMed PMID: 33154166; PubMed Central PMCID: PMC7948972.).

(DOCX)

Click here for additional data file. (28.4KB, docx)
S1 Fig. Correlation between annual fracture rate (FR) and Z-scores of TBS, BMAD, and BMDHAZ in non-severe OI participants without individuals with Sillence type Ⅲ (n = 37).

(TIF)

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S2 Fig. Comparison of the annual fracture rate and height Z-score (Ht-SD) between OI participants harboring nonsense and frameshift variants in COL1A1 causing haploinsufficient defect (HI, n = 17) and glycine substitution (GS) either in COL1A1 or COL1A2 causing severe phenotype (n = 9).

(TIF)

Click here for additional data file. (120KB, tif)
S3 Fig. Correlation between Z-scores of BMAD and BMDHAZ in all participants (n = 42) and in individuals with glycine substitution either in COL1A1 or COL1A2 (n = 9).

(TIF)

Click here for additional data file. (157.6KB, tif)

Acknowledgments

We would like to thank Ms. Satomi Okamura, Mr. Kazutaka Nishio, and Mr. Eisuke Hida for assistance with the statistical review. We would also like to thank Editage (www.editage.com) for English language editing. Finally, we thank the study participants for consenting to participate in this study.

Data Availability

All relevant data are within the paper and its Supporting Information files (S1 Table).

Funding Statement

This study was supported by “the Japan Agency for Medical Research and Development: https://www.amed.go.jp/en/” (No. 22ek0109549h0002, 22bm0804006h0206 and J210705007) to K.O., “the Ministry of Health, Labor, and Welfare: https://www.mhlw.go.jp/english/” (No. 22FC1012) to T.Ku., and “the Japan Society for the Promotion of Science: https://www.jsps.go.jp/english/” (No. 21H02881) to K.O.. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Jovanovic M, Guterman-Ram G, Marini JC. Osteogenesis imperfecta: mechanisms and signaling pathways connecting classical and rare OI types. Endocr Rev. 2022;43: 61–90. doi: 10.1210/endrev/bnab017 , PubMed Central PMCID: PMC8755987. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Robinson ME, Rauch F. Mendelian bone fragility disorders. Bone. 2019;126: 11–17. Epub 20190427. doi: 10.1016/j.bone.2019.04.021 . [DOI] [PubMed] [Google Scholar]
  • 3.Marini JC. Osteogenesis imperfecta. Primer Metab Bone Dis Disord Mineral Metab. 2018: 871–877. [Google Scholar]
  • 4.Sillence DO, Senn A, Danks DM. Genetic heterogeneity in osteogenesis imperfecta. J Med Genet. 1979;16: 101–116. doi: 10.1136/jmg.16.2.101 PubMed Central PMCID: PMC1012733. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Mortier GR, Cohn DH, Cormier-Daire V, Hall C, Krakow D, Mundlos S, et al. Nosology and classification of genetic skeletal disorders: 2019 revision. Am J Med Genet A. 2019;179: 2393–2419. Epub 20191021. doi: 10.1002/ajmg.a.61366 . [DOI] [PubMed] [Google Scholar]
  • 6.Willing MC, Deschenes SP, Slayton RL, Roberts EJ. Premature chain termination is a unifying mechanism for COL1A1 null alleles in osteogenesis imperfecta type I cell strains. Am J Hum Genet. 1996;59: 799–809. , PubMed Central PMCID: PMC1914787. [PMC free article] [PubMed] [Google Scholar]
  • 7.Marini JC, Forlino A, Cabral WA, Barnes AM, San Antonio JD, Milgrom S, et al. Consortium for osteogenesis imperfecta mutations in the helical domain of type I collagen: regions rich in lethal mutations align with collagen binding sites for integrins and proteoglycans. Hum Mutat. 2007;28: 209–221. doi: 10.1002/humu.20429 , PubMed Central PMCID: PMC4144349. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Glorieux FH. Osteogenesis imperfecta. Best Pract Res Clin Rheumatol. 2008;22: 85–100. doi: 10.1016/j.berh.2007.12.012 . [DOI] [PubMed] [Google Scholar]
  • 9.Rauch F, Glorieux FH. Osteogenesis imperfecta. Lancet. 2004;363: 1377–1385. doi: 10.1016/S0140-6736(04)16051-0 . [DOI] [PubMed] [Google Scholar]
  • 10.Forlino A, Marini JC. Osteogenesis imperfecta. Lancet. 2016;387: 1657–1671. Epub 20151103. doi: 10.1016/S0140-6736(15)00728-X , PubMed Central PMCID: PMC7384887. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Cepollaro C, Gonnelli S, Pondrelli C, Montagnani A, Martini S, Bruni D, et al. Osteogenesis imperfecta: bone turnover, bone density, and ultrasound parameters. Calcif Tissue Int. 1999;65: 129–132. doi: 10.1007/s002239900670 . [DOI] [PubMed] [Google Scholar]
  • 12.Reinus WR, McAlister WH, Schranck F, Chines A, Whyte MP. Differing lumbar vertebral mineralization rates in ambulatory pediatric patients with osteogenesis imperfecta. Calcif Tissue Int. 1998;62: 17–20. doi: 10.1007/s002239900387 . [DOI] [PubMed] [Google Scholar]
  • 13.Zemel BS, Leonard MB, Kelly A, Lappe JM, Gilsanz V, Oberfield S, et al. Height adjustment in assessing dual energy x-ray absorptiometry measurements of bone mass and density in children. J Clin Endocrinol Metab. 2010;95: 1265–1273. Epub 20100126. doi: 10.1210/jc.2009-2057 , PubMed Central PMCID: PMC2841534. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Crabtree NJ, Arabi A, Bachrach LK, Fewtrell M, El-Hajj Fuleihan G, Kecskemethy HH, et al. Dual-energy X-ray absorptiometry interpretation and reporting in children and adolescents: the revised 2013 ISCD Pediatric Official Positions. J Clin Densitom. 2014;17: 225–242. Epub 20140329. doi: 10.1016/j.jocd.2014.01.003 . [DOI] [PubMed] [Google Scholar]
  • 15.Carter DR, Bouxsein ML, Marcus R. New approaches for interpreting projected bone densitometry data. J Bone Miner Res. 1992;7: 137–145. doi: 10.1002/jbmr.5650070204 . [DOI] [PubMed] [Google Scholar]
  • 16.Rauch F, Lalic L, Roughley P, Glorieux FH. Relationship between genotype and skeletal phenotype in children and adolescents with osteogenesis imperfecta. J Bone Miner Res. 2010;25: 1367–1374. doi: 10.1359/jbmr.091109 . [DOI] [PubMed] [Google Scholar]
  • 17.Diacinti D, Pisani D, Cipriani C, Celli M, Zambrano A, Diacinti D, et al. Vertebral fracture assessment (VFA) for monitoring vertebral reshaping in children and adolescents with osteogenesis imperfecta treated with intravenous neridronate. Bone. 2021;143: 115608. Epub 20200820. doi: 10.1016/j.bone.2020.115608 . [DOI] [PubMed] [Google Scholar]
  • 18.Bouxsein ML. Bone quality: where do we go from here? Osteoporos Int. 2003;14;Suppl 5: S118–S127. Epub 20030829. doi: 10.1007/s00198-003-1489-x . [DOI] [PubMed] [Google Scholar]
  • 19.Kulak CA, Dempster DW. Bone histomorphometry: a concise review for endocrinologists and clinicians. Arq Bras Endocrinol Metabol. 2010;54: 87–98. doi: 10.1590/s0004-27302010000200002 . [DOI] [PubMed] [Google Scholar]
  • 20.Nishiyama KK, Shane E. Clinical imaging of bone microarchitecture with HR-pQCT. Curr Osteoporos Rep. 2013;11: 147–155. doi: 10.1007/s11914-013-0142-7 , PubMed Central PMCID: PMC4102136. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Vanleene M, Porter A, Guillot PV, Boyde A, Oyen M, Shefelbine S. Ultra-structural defects cause low bone matrix stiffness despite high mineralization in osteogenesis imperfecta mice. Bone. 2012;50: 1317–1323. Epub 20120316. doi: 10.1016/j.bone.2012.03.007 , PubMed Central PMCID: PMC3407875. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Coleman RM, Aguilera L, Quinones L, Lukashova L, Poirier C, Boskey A. Comparison of bone tissue properties in mouse models with collagenous and non-collagenous genetic mutations using FTIRI. Bone. 2012;51: 920–928. Epub 20120815. doi: 10.1016/j.bone.2012.08.110 , PubMed Central PMCID: PMC3583571. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Rauch F, Land C, Cornibert S, Schoenau E, Glorieux FH. High and low density in the same bone: a study on children and adolescents with mild osteogenesis imperfecta. Bone. 2005;37: 634–641. Epub 20050819. doi: 10.1016/j.bone.2005.06.007 . [DOI] [PubMed] [Google Scholar]
  • 24.Pazzaglia UE, Congiu T, Brunelli PC, Magnano L, Benetti A. The long bone deformity of osteogenesis imperfecta III: analysis of structural changes carried out with scanning electron microscopic morphometry. Calcif Tissue Int. 2013;93: 453–461. Epub 20130809. doi: 10.1007/s00223-013-9771-1 . [DOI] [PubMed] [Google Scholar]
  • 25.Imbert L, Aurégan JC, Pernelle K, Hoc T. Microstructure and compressive mechanical properties of cortical bone in children with osteogenesis imperfecta treated with bisphosphonates compared with healthy children. J Mech Behav Biomed Mater. 2015;46: 261–270. Epub 20150216. doi: 10.1016/j.jmbbm.2014.12.020 . [DOI] [PubMed] [Google Scholar]
  • 26.Imbert L, Aurégan JC, Pernelle K, Hoc T. Mechanical and mineral properties of osteogenesis imperfecta human bones at the tissue level. Bone. 2014;65: 18–24. Epub 20140505. doi: 10.1016/j.bone.2014.04.030 . [DOI] [PubMed] [Google Scholar]
  • 27.Albert C, Jameson J, Toth JM, Smith P, Harris G. Bone properties by nanoindentation in mild and severe osteogenesis imperfecta. Clin Biomech (Bristol, Avon). 2013;28: 110–116. Epub 20121107. doi: 10.1016/j.clinbiomech.2012.10.003 . [DOI] [PubMed] [Google Scholar]
  • 28.Fan Z, Smith PA, Harris GF, Rauch F, Bajorunaite R. Comparison of nanoindentation measurements between osteogenesis imperfecta Type III and Type IV and between different anatomic locations (femur/tibia versus iliac crest). Connect Tissue Res. 2007;48: 70–75. doi: 10.1080/03008200601090949 . [DOI] [PubMed] [Google Scholar]
  • 29.Silva BC, Broy SB, Boutroy S, Schousboe JT, Shepherd JA, Leslie WD. Fracture risk prediction by non-BMD DXA measures: the 2015 ISCD official positions Part 2: Trabecular bone score. J Clin Densitom. 2015;18: 309–330. doi: 10.1016/j.jocd.2015.06.008 . [DOI] [PubMed] [Google Scholar]
  • 30.Adams JE, Engelke K, Zemel BS, Ward KA. Quantitative computer tomography in children and adolescents: the 2013 ISCD Pediatric Official Positions. J Clin Densitom. 2014;17: 258–274. doi: 10.1016/j.jocd.2014.01.006 . [DOI] [PubMed] [Google Scholar]
  • 31.Sakka SD, Cheung MS. Management of primary and secondary osteoporosis in children. Ther Adv Musculoskelet Dis. 2020;12:1759720X20969262. Epub 20201102. doi: 10.1177/1759720X20969262 , PubMed Central PMCID: PMC7649886. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Silva BC, Leslie WD, Resch H, Lamy O, Lesnyak O, Binkley N, et al. Trabecular bone score: a noninvasive analytical method based upon the DXA image. J Bone Miner Res. 2014;29: 518–530. doi: 10.1002/jbmr.2176 . [DOI] [PubMed] [Google Scholar]
  • 33.Winzenrieth R, Michelet F, Hans D. Three-dimensional (3D) microarchitecture correlations with 2D projection image gray-level variations assessed by trabecular bone score using high-resolution computed tomographic acquisitions: effects of resolution and noise. J Clin Densitom. 2013;16: 287–296. Epub 20120630. doi: 10.1016/j.jocd.2012.05.001 . [DOI] [PubMed] [Google Scholar]
  • 34.Hans D, Barthe N, Boutroy S, Pothuaud L, Winzenrieth R, Krieg MA. Correlations between trabecular bone score, measured using anteroposterior dual-energy X-ray absorptiometry acquisition, and 3-dimensional parameters of bone microarchitecture: an experimental study on human cadaver vertebrae. J Clin Densitom. 2011;14: 302–312. Epub 20110702. doi: 10.1016/j.jocd.2011.05.005 . [DOI] [PubMed] [Google Scholar]
  • 35.Roux JP, Wegrzyn J, Boutroy S, Bouxsein ML, Hans D, Chapurlat R. The predictive value of trabecular bone score (TBS) on whole lumbar vertebrae mechanics: an ex vivo study. Osteoporos Int. 2013;24: 2455–2460. Epub 20130307. doi: 10.1007/s00198-013-2316-7 . [DOI] [PubMed] [Google Scholar]
  • 36.Kocijan R, Muschitz C, Haschka J, Hans D, Nia A, Geroldinger A, et al. Bone structure assessed by HR-pQCT, TBS and DXL in adult patients with different types of osteogenesis imperfecta. Osteoporos Int. 2015;26: 2431–2440. Epub 20150509. doi: 10.1007/s00198-015-3156-4 . [DOI] [PubMed] [Google Scholar]
  • 37.Rehberg M, Winzenrieth R, Hoyer-Kuhn H, Duran I, Schoenau E, Semler O. TBS as a tool to differentiate the impact of antiresorptives on Cortical and trabecular bone in children with Osteogenesis Imperfecta. J Clin Densitom. 2019;22: 229–235. Epub 20180908. doi: 10.1016/j.jocd.2018.09.001 . [DOI] [PubMed] [Google Scholar]
  • 38.Kalkwarf HJ, Shepherd JA, Hans D, Gonzalez Rodriguez E, Kindler JM, Lappe JM, et al. Trabecular bone score reference values for children and adolescents according to age, sex, and ancestry. J Bone Miner Res. 2022. Epub 20220203;37: 776–785. doi: 10.1002/jbmr.4520 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Kindler JM, Lappe JM, Gilsanz V, Oberfield S, Shepherd JA, Kelly A, et al. Lumbar spine bone mineral apparent density in children: results from the bone mineral density in childhood study. J Clin Endocrinol Metab. 2019;104: 1283–1292. doi: 10.1210/jc.2018-01693 , PubMed Central PMCID: PMC6397436. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Fraga MM, de Sousa FP, Szejnfeld VL, de Moura Castro CH, de Medeiros Pinheiro M, Terreri MT. Trabecular bone score (TBS) and bone mineral density (BMD) analysis by dual X-ray absorptiometry (DXA) in healthy Brazilian children and adolescents: normative data. Arch Osteoporos. 2023;18: 82. Epub 20230615. doi: 10.1007/s11657-023-01291-1 . [DOI] [PubMed] [Google Scholar]
  • 41.Guo M, Zemel BS, Hawkes CP, Long J, Kelly A, Leonard MB, et al. Sarcopenia and preserved bone mineral density in paediatric survivors of high-risk neuroblastoma with growth failure. J Cachexia Sarcopenia Muscle. 2021;12: 1024–1033. Epub 20210629. doi: 10.1002/jcsm.12734 , PubMed Central PMCID: PMC8350210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Dahan-Oliel N, Collins J, Rauch D, Bukovy G, Hamdy R, Rauch F. Bone densities and bone geometry in children and adolescents with arthrogryposis. Bone. 2020;137: 115454. Epub 20200525. doi: 10.1016/j.bone.2020.115454 . [DOI] [PubMed] [Google Scholar]
  • 43.Ohata Y, Takeyari S, Nakano Y, Kitaoka T, Nakayama H, Bizaoui V, et al. Correction to: comprehensive genetic analyses using targeted next-generation sequencing and genotype-phenotype correlations in 53 Japanese patients with osteogenesis imperfecta. Osteoporos Int. 2020;31: 1185. doi: 10.1007/s00198-020-05396-y , PubMed Central PMCID: PMC7237517. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Isojima T, Kato N, Ito Y, Kanzaki S, Murata M. Growth standard charts for Japanese children with mean and standard deviation (SD) values based on the year 2000 national survey. Clin Pediatr Endocrinol. 2016;25: 71–76. Epub 20160428. doi: 10.1297/cpe.25.71 , PubMed Central PMCID: PMC4860518. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Kalkwarf HJ, Zemel BS, Yolton K, Heubi JE. Bone mineral content and density of the lumbar spine of infants and toddlers: influence of age, sex, race, growth, and human milk feeding. J Bone Miner Res. 2013;28: 206–212. doi: 10.1002/jbmr.1730 , PubMed Central PMCID: PMC3527676. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Zemel BS, Kalkwarf HJ, Gilsanz V, Lappe JM, Oberfield S, Shepherd JA, et al. Revised reference curves for bone mineral content and areal bone mineral density according to age and sex for black and non-black children: results of the bone mineral density in childhood study. J Clin Endocrinol Metab. 2011;96: 3160–3169. Epub 20110914. doi: 10.1210/jc.2011-1111 , PubMed Central PMCID: PMC3200252. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Crabtree NJ, Shaw NJ, Bishop NJ, Adams JE, Mughal MZ, Arundel P, et al. Amalgamated reference data for size-adjusted bone densitometry measurements in 3598 children and young adults-the Alphabet study. J Bone Miner Res. 2017;32: 172–180. Epub 20160907. doi: 10.1002/jbmr.2935 , PubMed Central PMCID: PMC5453244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Hoyer-Kuhn H, Höbing L, Cassens J, Schoenau E, Semler O. Children with severe Osteogenesis imperfecta and short stature present on average with normal IGF-I and IGFBP-3 levels. J Pediatr Endocrinol Metab. 2016;29: 813–818. doi: 10.1515/jpem-2015-0385 . [DOI] [PubMed] [Google Scholar]
  • 49.Forlino A, Cabral WA, Barnes AM, Marini JC. New perspectives on osteogenesis imperfecta. Nat Rev Endocrinol. 2011;7: 540–557. Epub 20110614. doi: 10.1038/nrendo.2011.81 , PubMed Central PMCID: PMC3443407. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Bains JS, Carter EM, Citron KP, Boskey AL, Shapiro JR, Steiner RD, et al. A multicenter observational cohort study to evaluate the effects of bisphosphonate exposure on bone mineral density and other health outcomes in osteogenesis imperfecta. JBMR Plus. 2019;3: e10118. Epub 20190107. doi: 10.1002/jbm4.10118 , PubMed Central PMCID: PMC6524673. [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Heather Macdonald

3 May 2023

PONE-D-23-03588Association of trabecular bone score and bone mineral apparent density with the severity of bone fragility in children and adolescents with osteogenesis imperfecta: A cross-sectional studyPLOS ONE

Dear Dr. Ozono,

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Additional Editor Comments:

As noted by Reviewer 1, assessing bone outcomes in children and adolescents with osteogenesis imperfecta is challenging for a number of reasons, and while the authors are to be commended for taking on this challenge, there are a number of limitations that need to be addressed more thoroughly. In particular, the authors are encouraged to more clearly state their study objectives, and ensure that the steps in their statistical analysis align appropriately with these objectives. Statistical review may be warranted, particularly given the small sample size (please clarify in the methods whether this was a convenience sample, or whether a sample size calculation was performed a priori). I am also unclear why there are more supplementary tables and figures than are included in the main paper.

Additional comments

1. Subjects, participants and patients are used interchangeably throughout the methods/results/figures - suggest using participants throughout.

2. Abstract: Consider modifying to include actual numerical results in the abstract.

3. Lines 58-61: Please clarify that DXA measures two-dimensional areal BMD, which as Reviewer 1 highlights is strongly influenced by body size.

4. Line 61: Quotes are not needed around the name.

5. Line 66: Change "are also" to "is also"

6. Lines 67-68: The authors state that these approaches may be useful for evaluation of bone fragility in children and youth with OI. If they haven't been used in a previous study of children/youth with OI, please clarify this, or provide the appropriate references if they have been used previously.

7. Line 79: The radiation dose is comparable between HR-pQCT and DXA - please update the text accordingly (see papers such as Pezzuti et al., JPEM, https://doi.org/10.1515/jpem-2016-0252).

8. Line 105: Regarding the cohort of 47 OI participants, were these all of the OI patients who underwent DXA at the Osaka University Hospital, or do the 47 represent the proportion who consented to participate in study? If it is the latter, please clarify how many individuals were invited to participate.

9. Line 107: How many participants had multiple DXA scans between 2015 and 2022? Depending on the number of participants with repeat scans, have the authors explored any longitudinal analyses?

10. Line 113: How many participants were excluded due to an inaccurate DXA scan?

11. Lines 115-6: Change to All individuals in the cohort were of Asian ethnicity and resided in Japan. Related to this, how many children of Asian ethnicity were included in the non-African American reference dataset published by Kawlkwarf et al?

12. Lines 125-6: I assume height and weight were measured at the time of the DXA scan? Please clarify in the text, and mention that BMI was calculated.

13. Line 127: The authors mention that HAZ were calculated using reference data from the JSPE (quotes not needed), but weight-for-age and BMI-for-age Z scores are provided in Table 1- were they calculated using the same reference data? Please add this information.

14. Line 131: Did participants report their fracture history at the time of the DXA scan? And for participants who underwent repeat DXA scans during 2015-2022, did they report their fracture history at each DXA scan? And earlier in the methods, the authors mentioned that the OI diagnosis was based on low trauma fractures, so it would help to clarify if fracture history only included low/minimal trauma fractures, or low, moderate and high trauma fractures. It would also help to report the total number of fractures in the cohort.

15. Line 137: Consider starting the sentence with Areal BMD (instead of the abbreviation), and provide the units for aBMD. Did the same technician acquire and analyze all DXA scans? How was cross-calibration confirmed? What is the %CV for the DXA outcomes and TBS outcomes? In addition, the TBS reference data published by Kawlkwarf et al. was based on TBS iNsight software pre-release version 4.0, not version 3.03 as used by the authors in the present study - please discuss how that impacts this analysis.

16. Statistics: As noted by both Reviewers, additional details are required to ensure that the description of the statistical analysis aligns with the study objectives. Please clarify how regression assumptions were checked.

17. Table 1: Change gender to sex unless the authors specifically asked about gender/socially constructed roles instead of biological sex.

18. Line 188-190: Modify wording since "effect" implies causation, which is not appropriate in this cross-sectional study.

19. Lines 230-231: I don't follow the wording of this sentence.

20. Line 243: Change to: needs to be elucidated.

21. Was the study adequately powered to conduct the subgroup analyses?  

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Association of trabecular bone score and bone mineral apparent density with the severity of bone fragility in children and adolescents with osteogenesis imperfecta

This study examines the relationship between fracture rates and different DXA measures of bone density in OI adolescents.

General:

This study tackles a very challenging topic. Bone density in growing bone is challenging because of the limitations of projection measures when size is changing. On top of that, adding bones that do not grow as they should, bones that are pathologic, and bones that have been treated by various bisphosphonates for various amounts of time, you have a very challenging area of study. The authors are applauded for finding any correlations in the data set at all! It would help to clarify the challenges in the introduction. Be explicit that predicting fracture rates must combine estimates of strength (density) and quality (structure and material properties). Explain why projection measures of density of different size objects is challenging. Explain that DXA is a common tool for assessing osteoporosis risk in elderly but poses challenges when applied to children of all different sizes, especially when the defect is a molecular defect which likely alters material properties, not just density (how much bone) and structure (where the bone is). This will help make it stand out how challenging it is. The first paragraph of the intro just hints at “confounding factor of short stature”. Explain it more.

The underlying problem in a projection measurement is the thickness of the bone affects the projected density. It is therefore not surprising that the measure that adjusts for the thickness of the bone (BMAD) best correlates. Other measures of adjusting (age-adjusted, height-adjusted, height-for-age Zscore) are using alternative measures of size to try to do this.

What is the clinical question? Severity of fracture risk? (line 30) “severity evaluation in children and adolescents is hard because of abnormalities in bone quality that BMD cannot accurately assess”. It is true, bone quality cannot be assessed with a density measure. Significant research has shown density correlates well with strength but not with “quality”, which generally means everything but strength. Especially in OI, the underlying problem is the material itself so no matter how much bone they have and how it is arranged at the macro level, there will be a material integrity problem. Fracture risks is not directly quality, however. It is a combination of strength (density) and quality (arrangement, composition, material properties). It seems like what you are asking is: can we predict fracture risk with a density measure of some sort? You should bring this out in the abstract. It is currently phrased “to evaluate the clinical applicability [of adjusted density measures] we analyzed associations.” Be more direct: we determined correlations between different DXA measures of bone density and fracture risk.

Explain the rationale for analyzing particular genetic variants separately. Would you expect specific variants to be more correlated with density measures than others? Why? Justify this briefly in the intro.

Abstract:

Focus the abstract to the question: which density measures best correlates with fracture risk?

Intro:

Line 59: “BMD is lower in OI bones” Is this because the bones are smaller? Beware!

Line 95: “To evaluate clinical applicability” It seems like you are saying: “finally data sets are available for bone density in children that adjust for size; let’s see if they work for pathologic bone in which density is secondary to a material defect.” Is that the goal? Have these data sets (Z scores) been used for other non-pathologic bone density problems (eating disorders, amenorrhea, etc.) in which the bone tissue itself is fine there is just very little of it. Do the scores predict fracture rates in these populations (who are granted much less prone to fracture)?

Line 78: What is meant by “bone biospies are too professional to be performed widely”?

Line 89: “TBS can be performed to evaluate bone quality in OI children” Perhaps instead “TBS can complement standard density measures in assessing skeletal integrity”

Line 170: How they diagnosed with OI if they do not have a genetic variant? Is it purely a symptomatic diagnosis?

Line 188: Change “affected” to “correlated”. Are these slist

Line 190: “tended to show” This is overly generous for a correlation of 0.27 and a p value of 0.07. Even if it were significant, it is still a poor correlation.

Supplementary Figure 2 is important. Can you bring it to main text? Then you could leave out stats from the paragraph and it will read much easier. Make the BMAD correlation bold in the table as that is the only one that is significant (and negative).

Line 190: Why do you think BMADHaz had a positive effect? Isn’t that worrying? Wat is the issue with this measure do you think? Discuss in discussion.

Line 192: “We analyzed the relationship of fracture rate with Z-scores.” How is this different from line 186 “regression analysis for fracture rate with Z-scores”. You need to make it clear that line 186 is the multiple regression analysis and line 192 is the simple linear regression. What does it mean that BMAD has a strong association (multiple regression) but a weak correlation (simple linear regression)?

Line 225: “usefulness”? Why do you single out BMADHaz? Just say “For the first time we determined correlations between density measures Z-scores of TBS, BMAD, and BMADHaz with fracture risk.”

Line 243: How would you elucidate these? With HRpqCT or pqCT to determine structural deficiencies?

Line 252: “abnormality of bone size have a strong impact on BMD” This makes it sound like having small bones means you have low BMD. This is an artefact of the imaging technique and using a projection method. This should be made more clear.

Line 253: “BMD itself” It it is adjusted for size, then it isn’t BMD, it is BMAD, right?

Discussion: Why do you think that for haploinsufficiency fractures were not correlated with BMAD but glycine fractures were? Whereas haploinsufficiency fractures were correlated with TBS but glycine fractures were not?

Reviewer #2: This study evaluates the recently described Z-scores for several parameters (TBS and BMAD) calculated from retrospective DEXA results in children and adolescents with OI. It compares fracture rates with these measures and the more standard DEXA parameter: height and age-adjusted BMD Z-score. It also analyses the correlation between the Z-scores and fracture rates in mild and severe OI subgroups. There was a negative correlation of fracture rate with TBS in the patients with mild OI and BMAD in patients with severe OI.

Abstract and Introduction:

1. Line 27: the way this is phrased is confusing “The height-for-age Z-score (HAZ)-adjusted BMD-for-age Z-score (BMDHAZ)”. I would suggest simplifying it to something like: “the height-adjusted BMD Z-score for age (BMDHAZ)” or the “height-for-age BMD Z-score (BMDHAZ)”

2. Line 78: suggest changing the word “professional” to “specialized”

3. Line 96: suggest changing “bone fragility” to “fracture rate”

Results:

1. Table 1: this is a little difficult to read. Suggest perhaps breaking up the OI participants into the subgroups you use for the later analysis. Having the number and demographics (average heights, fracture rates and DEXA parameters etc) of the patients with haploinsufficiency vs glycine mutations in this table would be useful.

2. Although you explain your reasoning for performing both the multiple regression and the simple linear regression (lines 153-155), the conflicting results are confusing. The multiple regression found the BMAD Z-score was significantly negatively correlated, and the BMD-HAZ was significantly positively correlated with fracture rates. However, the simple linear regression found no correlation for either of these DEXA parameters (in the whole cohort). A statistician’s input may be helpful here to determine the most appropriate test to present.

Figures:

1. I don’t think Figure 1 is required in the main paper, as it is only to prove why you are using these genetic results to represent your “mild” vs “severe” OI phenotype groups. You can simply state the result in the text and include the figure with Figure S3.

2. Figure 2 and 3: suggest use “per year” instead of “/yr.”

3. I suggest Figure S1 be included in the main paper (it shows no correlation when the entire cohort is analysed using simple linear regression) as Fig 2 and 3 (the “mild/haploinsufficiency” and “severe/glycine mutation” subgroups) should be interpreted with this in mind.

Discussion:

1. Lines 228-229: A result is listed in the discussion that is not mentioned earlier in the paper. Suggest adding this to the results section.

2. The postulated explanation for why TBS only correlates in mild OI due to haploinsufficiency is interesting. It would be interesting to know if this correlation simply relates to the amount of bisphosphonate treatment the patient has received (with more severe cases often receiving more treatment) – this is partially addressed later in the discussion

3. Line 251: suggest changing “evaluated the” to “correlated with”

4. Line 257-259: it would be useful to consider that a measure (BMAD) that accounts for the size of the bones results in a stronger correlation with fracture rates due to the mechanical properties of the bone (rather than just providing a better estimate of density). Small/narrow bones break more easily than large bones, even if they have the same density. BMAD might reflect this.

5. Line 269: missing words “we analysed fracture rate as a (marker of) severity”

6. Line 270-273: the difference fracture rates with age are not just due to activity levels. Growth rate and puberty also play a role.

General comments:

1. Suggest using the wording “children and adolescents with OI” rather than “OI children and adolescents” throughout (e.g. line 86 and 89)

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PLoS One. 2023 Aug 29;18(8):e0290812. doi: 10.1371/journal.pone.0290812.r002

Author response to Decision Letter 0

5 Jul 2023

RESPONSE TO REVIEWERS

Additional Editor Comments:

As noted by Reviewer 1, assessing bone outcomes in children and adolescents with osteogenesis imperfecta is challenging for a number of reasons, and while the authors are to be commended for taking on this challenge, there are a number of limitations that need to be addressed more thoroughly. In particular, the authors are encouraged to more clearly state their study objectives, and ensure that the steps in their statistical analysis align appropriately with these objectives. Statistical review may be warranted, particularly given the small sample size (please clarify in the methods whether this was a convenience sample, or whether a sample size calculation was performed a priori). I am also unclear why there are more supplementary tables and figures than are included in the main paper.

Response: Thank you for your thorough review and important comments. We agree with the comments from the Editor and Reviewer 1 that it is challenging to assess growing bone with molecular defects. According to the suggestion of Reviewer 1, we have added sentences to clarify the difficulty of this study as follows. “However, bone density measurement in growing bone is challenging due to the inherent complexities of measuring the projection density of objects of varying sizes. Moreover, the thickness of the bone itself affects the projected density.”

We also agree with the comment that when predicting fracture rates, we must combine estimates of the strength (density) and quality (structure and material properties) of bones, especially in pathologic bones. To further explain this, we have added the following description in the introduction section, “To estimate the bone fragility, it is necessary to perform a combined evaluation of bone strength (measured by bone density) and bone quality (reflected by the bone structure and material properties), especially in bones with underlying molecular defects that are associated with altered material properties, such as in patients with OI.”

To clarify the study objective and clinical questions as Reviewer 1 pointed out, we have revised the description regarding clinical questions in the abstract as follows. “To evaluate which density measurements show the highest correlation with fracture risk, we analyzed the associations between the Z-scores of TBS, BMAD, and BMDHAZ, fracture rate, and genetic variants.”

As the Editor and Reviewer 2 recommended, the manuscript underwent a statistical review by statisticians. They pointed out that performing multiple regression analysis with the whole cohort can be misleading and should be deleted from our analysis because some parameters are distributed non-normally. Thus, we have excluded the multiple regression analysis and related descriptions. We also discussed with the statisticians about the small sample number, especially in the subgroup analysis. To address the concerns regarding sampling, we have clarified the inclusion criteria and included only participants harboring any pathogenic variants. Finally, we have changed the original Supplemental Figure 1 to Figure 1 and created Table 2 as Reviewer 2 suggested. Thanks to the thorough review and constructive opinions, we believe that the manuscript has been improved and refined.

Additional comments

1. Subjects, participants and patients are used interchangeably throughout the methods/results/figures - suggest using participants throughout.

Response: Thank you for your suggestion. We have corrected “subjects” and “patients” to “participants” throughout the methods, results, and figures.

2. Abstract: Consider modifying to include actual numerical results in the abstract.

Response: We have included the numerical results in the abstract as follows. In lines 46–48, we have added (r = -0.50, p = 0.042) and (r = -0.74, p = 0.022), respectively.

3. Lines 58-61: Please clarify that DXA measures two-dimensional areal BMD, which as Reviewer 1 highlights is strongly influenced by body size.

Response: Thank you for your suggestion. As Reviewer 1 suggested, we have added sentences as follows to explain that bone density evaluation in growing bone is challenging. “However, bone density measurement in growing bone is challenging due to the inherent complexities of measuring the projection density of objects of varying sizes. Moreover, the thickness of the bone itself affects the projected density.”

4. Line 61: Quotes are not needed around the name.

Response: As per your comment, we have excluded the quotes from the description of “The International Society for Clinical Densitometry”.

5. Line 66: Change "are also" to "is also"

Response: We have changed “are also” to “is also” in line 80 , which is highlighted with red font.

6. Lines 67-68: The authors state that these approaches may be useful for evaluation of bone fragility in children and youth with OI. If they haven't been used in a previous study of children/youth with OI, please clarify this, or provide the appropriate references if they have been used previously.

Response: Thank you for your suggestion. Rauch et al. analyzed the relationship between genotype and lumbar spinal areal BMD Z-score with adjustment for age, sex, and height Z-scores. (JBMR 2010: 1367) Diacinti et al. evaluated the mean bone mineral apparent density (BMAD) before and after intravenous neridronate therapy in children and adolescents with OI (Bone. 2021: 115608). However, they did not assess the fracture rate, as we have evaluated. We have added the following sentences to the revised manuscript with the appropriate references. “Previously, two studies evaluated children and adolescents with OI using different parameters. Rauch et al. studied the relationship between genotype and lumbar spinal areal BMD Z-score with adjustment for age, sex, and height Z-scores, while, Diacinti et al. examined the mean BMAD before and after intravenous neridronate therapy. Although both studies did not assess bone fragility, these approaches may be useful for its evaluation in children and adolescents with OI.”

7. Line 79: The radiation dose is comparable between HR-pQCT and DXA - please update the text accordingly (see papers such as Pezzuti et al., JPEM, https://doi.org/10.1515/jpem-2016-0252).

Response: Thank you for enlightening us on this matter. Based on your comments, we have deleted the description stating that HRpQCT employs a greater radiation dose than DXA.

8. Line 105: Regarding the cohort of 47 OI participants, were these all of the OI patients who underwent DXA at the Osaka University Hospital, or do the 47 represent the proportion who consented to participate in study? If it is the latter, please clarify how many individuals were invited to participate.

Response: Thank you for your suggestion. As Reviewer 1 suggested, we have included the participants who were diagnosed with OI using genetic testing. We have described this information in the revised manuscript as follows. “Between 2015 to 2022, a total of 44 participants who had received a genetic diagnosis of OI underwent DXA at Osaka University, ranging in age from 5 to 20 years. Among them, two participants were excluded from enrollment: one due to the unavailability of a written informed consent form, and another because of the inability to conduct an accurate DXA scan following lumbar spine surgery for scoliosis correction. Thus, overall, 42 participants with OI harboring pathogenic variants were included in this study. Participants with lumbar fractures were excluded from the study since they did not undergo DXA.”

9. Line 107: How many participants had multiple DXA scans between 2015 and 2022? Depending on the number of participants with repeat scans, have the authors explored any longitudinal analyses?

Response: Among 42 participants, 39 had multiple DXA scans during the study period. We have added this information to the revised manuscript as follows. “If DXA was performed several times in an individual participant during this observational period, we selected the latest data and analyzed them (N = 39).” Unfortunately, we did not perform longitudinal analyses because this is a cross-sectional study. We are interested in performing such analyses and had described it as a study limitation as follows. “Therefore, in the future, it will be necessary to conduct a longitudinal study to analyze the impact of treatments on our findings.”

10. Line 113: How many participants were excluded due to an inaccurate DXA scan?

Response: Thank you for your comment. One participant was excluded because of the inability to conduct an accurate DXA scan following lumbar spine surgery for scoliosis correction. We have included this information in the revised manuscript (kindly see response to comment 8).

11. Lines 115-6: Change to All individuals in the cohort were of Asian ethnicity and resided in Japan. Related to this, how many children of Asian ethnicity were included in the non-African American reference dataset published by Kawlkwarf et al?

Response: Thank you for your comment. Unfortunately, Kalkwarf et al. categorized the participants as having African ancestry or non-African ancestry based on parental report, and there was no detailed description on whether participants of Asian ethnicity were included in the non-African group. Kindler et al. reported that ancestry was categorized as black or non-black, the latter of which include people of European, Hispanic, Asian, and other ancestries. However, there was no description about the precise number of Asian participants. We have added the following sentences as a limitation of this study. “Although we calculated the Z-scores of TBS and BMAD based on the reference of non-African Americans, there was no information on how many Asian participants were included in the reference data.”

12. Lines 125-6: I assume height and weight were measured at the time of the DXA scan? Please clarify in the text, and mention that BMI was calculated.

Response: Thank you for your kind comment. As you assumed, we measured height and weight at the time of the DXA scan. We have corrected the sentence as follows to clearly state that BMI was calculated from these measurements, “When the participants underwent the DXA scan, their body weight (kg) and height (cm) were measured at our hospital using a digital electronic scale and stadiometer, respectively, and the body mass index (BMI) was calculated.”

13. Line 127: The authors mention that HAZ were calculated using reference data from the JSPE (quotes not needed), but weight-for-age and BMI-for-age Z scores are provided in Table 1- were they calculated using the same reference data? Please add this information.

Response: As you pointed out, we calculated weight-for-age and BMI-for-age Z scores from the same reference data from the JSPE. We have changed the sentence as follows. “HAZ and Z-scores of body weight and BMI were calculated based on reference data from The Japanese Society for Pediatric Endocrinology”. In the revised description, we have excluded the quotes.

14. Line 131: Did participants report their fracture history at the time of the DXA scan? And for participants who underwent repeat DXA scans during 2015-2022, did they report their fracture history at each DXA scan? And earlier in the methods, the authors mentioned that the OI diagnosis was based on low trauma fractures, so it would help to clarify if fracture history only included low/minimal trauma fractures, or low, moderate and high trauma fractures. It would also help to report the total number of fractures in the cohort.

Response: Thank you for your thorough review. As you commented, the participants reported their fracture history at every DXA scan between 2015-2022. We have added the phrase “at every DXA scan” in the fracture assessment. Among the 42 participants, we had actually included participants who had been diagnosed clinically based on a history of fractures with mild trauma. However, as Reviewer 1 suggested, we have included only the participants who had been diagnosed with OI through genetic testing. In the revised manuscript, we have removed the descriptions regarding the clinical diagnosis of OI. The total number of fractures in each participant was included in S1 Table.

15. Line 137: Consider starting the sentence with Areal BMD (instead of the abbreviation), and provide the units for aBMD. Did the same technician acquire and analyze all DXA scans? How was cross-calibration confirmed? What is the %CV for the DXA outcomes and TBS outcomes? In addition, the TBS reference data published by Kawlkwarf et al. was based on TBS iNsight software pre-release version 4.0, not version 3.03 as used by the authors in the present study - please discuss how that impacts this analysis.

Response: Thank you for your suggestion and comment. We have changed the start of the paragraph from aBMD to Areal BMD and added the unit (g/cm2). A single, professional technician performed and analyzed the DXA scans, and he has confirmed that the data measured by the Hologic Discovery A DXA scanner and Hologic Horizon A DXA scanner are identical. We have revised the description as follows, “All measurements were analyzed for bone mineral content, bone area, and aBMD at the department of radiology of our hospital by a professional technician.”

Unfortunately, we could not get any data regarding the difference between TBS iNsight version 4.0 and 3.03 because the version 4.0 software is a pre-release model. We have added the following description as a study limitation in the discussion section. “Second, the TBS reference data published by Kawlkwarf et al. was based on TBS iNsight software pre-release version 4.0. In this study, we analyzed the TBS by version 3.03 as we were unable to use the pre-release model; additionally, we could not evaluate the difference between these different models.”

16. Statistics: As noted by both Reviewers, additional details are required to ensure that the description of the statistical analysis aligns with the study objectives. Please clarify how regression assumptions were checked.

Response: As the Editor and Reviewer 2 recommended, the manuscript underwent a statistical review by statisticians. They pointed out that performing multiple regression analysis with the whole cohort can be misleading and should be deleted from our analysis because some parameters are distributed non-normally. Thus, we have excluded the multiple regression analysis and related descriptions.

17. Table 1: Change gender to sex unless the authors specifically asked about gender/socially constructed roles instead of biological sex.

Response: We have changed “gender” to “sex” in Table 1.

18. Line 188-190: Modify wording since "effect" implies causation, which is not appropriate in this cross-sectional study.

Response: Thank you for your thorough review. Following the statistical review, this sentence has been deleted (kindly see response to comment 16).

19. Lines 230-231: I don't follow the wording of this sentence.

Response: We apologize for the confusing description. We wished to explain that using pQCT, Rauch et al. reported there are high and low BMD areas interspersed within the same bone in children and adolescents with mild OI. To refine it, we have changed the sentence as follows, “Through pQCT evaluation, it was observed that areas of high and low BMD were interspersed within the same bone in children and adolescents with mild OI.”

20. Line 243: Change to: needs to be elucidated.

Response: Thank you for your correction. We have changed the sentence as follows, “However, the mechanism by which the haploinsufficient variants contribute to the observed abnormalities in OI bones need to be elucidated.”

21. Was the study adequately powered to conduct the subgroup analyses?

Response: Thank you for your comment. We have discussed this point with the statisticians during the statistical review. They concluded that we cannot exclude the possibility that the power was not enough, especially in the subgroup analysis as you said, due to the lack of calculation of the optimal sample number prior to conducting this study. Although they provided this feedback on our statistical analysis, they also acknowledged that the subgroup used in our study was appropriate since we had established clear eligibility criteria and enrolled as many participants as possible. They also recognized that our study has the potential to serve as a pilot study for future, larger studies with multiple institutions. We have added the following sentences as a study limitation in the discussion section. “However, we confirmed that the subgroup used in our study was statistically appropriate through statistical review and that our study has the potential to serve as a pilot study for future, larger, multi-center studies.”

Reviewer #1: Association of trabecular bone score and bone mineral apparent density with the severity of bone fragility in children and adolescents with osteogenesis imperfecta

This study examines the relationship between fracture rates and different DXA measures of bone density in OI adolescents.

General:

This study tackles a very challenging topic. Bone density in growing bone is challenging because of the limitations of projection measures when size is changing. On top of that, adding bones that do not grow as they should, bones that are pathologic, and bones that have been treated by various bisphosphonates for various amounts of time, you have a very challenging area of study. The authors are applauded for finding any correlations in the data set at all! It would help to clarify the challenges in the introduction. Be explicit that predicting fracture rates must combine estimates of strength (density) and quality (structure and material properties). Explain why projection measures of density of different size objects is challenging. Explain that DXA is a common tool for assessing osteoporosis risk in elderly but poses challenges when applied to children of all different sizes, especially when the defect is a molecular defect which likely alters material properties, not just density (how much bone) and structure (where the bone is). This will help make it stand out how challenging it is. The first paragraph of the intro just hints at "confounding factor of short stature". Explain it more.

The underlying problem in a projection measurement is the thickness of the bone affects the projected density. It is therefore not surprising that the measure that adjusts for the thickness of the bone (BMAD) best correlates. Other measures of adjusting (age-adjusted, height-adjusted, height-for-age Z score) are using alternative measures of size to try to do this.

What is the clinical question? Severity of fracture risk? (line 30) "severity evaluation in children and adolescents is hard because of abnormalities in bone quality that BMD cannot accurately assess". It is true, bone quality cannot be assessed with a density measure. Significant research has shown density correlates well with strength but not with "quality", which generally means everything but strength. Especially in OI, the underlying problem is the material itself so no matter how much bone they have and how it is arranged at the macro level, there will be a material integrity problem. Fracture risks is not directly quality, however. It is a combination of strength (density) and quality (arrangement, composition, material properties). It seems like what you are asking is: can we predict fracture risk with a density measure of some sort? You should bring this out in the abstract. It is currently phrased "to evaluate the clinical applicability [of adjusted density measures] we analyzed associations." Be more direct: we determined correlations between different DXA measures of bone density and fracture risk.

Response: Thank you for your thorough review and insightful suggestions. As you have suggested, we have added sentences as follows to explain that the evaluation of the bone density in growing bone is challenging, “However, bone density measurement in growing bone is challenging due to the inherent complexities of measuring the projection density of objects of varying sizes. Moreover, the thickness of the bone itself affects the projected density.”

We also agree with the comment that when predicting fracture rates, we must combine estimates of the strength (density) and quality (structure and material properties) of bones, especially in pathologic bones. To further explain this, we have added the following descriptions in the introduction section, “To estimate the bone fragility, it is necessary to perform a combined evaluation of bone strength (measured by bone density) and bone quality (reflected by the bone structure and material properties), especially in bones with underlying molecular defects that are associated with altered material properties, such as in patients with OI.”

To clarify the study objective and clinical questions as Reviewer 1 pointed out, we have revised the description regarding clinical questions in the abstract as follows. “To evaluate which density measurements show the highest correlation with fracture risk, we analyzed the associations between the Z-scores of TBS, BMAD, and BMDHAZ, fracture rate, and genetic variants.”

Explain the rationale for analyzing particular genetic variants separately. Would you expect specific variants to be more correlated with density measures than others? Why? Justify this briefly in the intro.

Response: We apologize for the lack of an explanation. We have added the following description in the introduction section. “It has been reported that a quantitative deficiency in collagen type I causes a mild form of OI, while a structural defect derived from glycine substitutions in COL1A1 or COL1A2 genes results in a more severe form.”

Abstract:

Focus the abstract to the question: which density measures best correlates with fracture risk?

Response: Thank you for your kind suggestion. We have corrected the sentence as follows, “To evaluate which density measurements show the highest correlation with fracture risk, we analyzed the associations between the Z-scores of TBS, BMAD, and BMDHAZ, fracture rate, and genetic variants.”

Intro:

Line 59: "BMD is lower in OI bones" Is this because the bones are smaller? Beware!

Response: Thank you for your thorough review. We corrected the sentence as follows to avoid being misleading. ““Evaluation of the bone mineral density (BMD) using dual-energy x-ray absorptiometry (DXA) can aid OI diagnosis, given that OI patients have significantly lower BMD levels compared to healthy individuals of the same age and sex.”

Line 95: "To evaluate clinical applicability" It seems like you are saying: "finally data sets are available for bone density in children that adjust for size; let's see if they work for pathologic bone in which density is secondary to a material defect." Is that the goal? Have these data sets (Z scores) been used for other non-pathologic bone density problems (eating disorders, amenorrhea, etc.) in which the bone tissue itself is fine there is just very little of it. Do the scores predict fracture rates in these populations (who are granted much less prone to fracture)?

Response: Thank you for your meaningful suggestion. Although Fraga et al. used the TBS Z-score, which we referred to in this manuscript, for healthy Brazilian children and adolescents, there is no publication in which the TBS Z-scores were used for other non-pathogenic bone density problems including eating disorders or amenorrhea. On the other hand, the BMAD Z-scores were used for cancer survivors (Guo et al., 2021) and arthrogryposis patients (Dahan-Oliel et al., 2020), to evaluate bone health conditions in children and adolescent populations. However, they did not analyze the relationship between the BMAD Z-scores and fracture rate. To include this background context, we have added the following descriptions with new references. “Fraga et al. evaluated the Z-score of TBS for healthy Brazilian children and adolescents. Additionally, the BMAD Z-score has been used for cancer survivors and arthrogryposis patients to assess bone health conditions in children and adolescent populations. However, the relationship between these scores and bone fragility were not evaluated in these studies, and no publication has yet assessed these Z-scores in children and adolescents with OI.”

Line 78: What is meant by "bone biopsies are too professional to be performed widely"?

Response: We apologize for the confusing wording. Reviewer 2 has also pointed this issue out and suggested to change the word of “professional” to “specialized”. According to the suggestion, we have modified the sentence as follows. “While the aforementioned findings provide the rationale for performing bone biopsy and HRpQCT routinely in OI assessment of children and adolescents, this approach is difficult to implement given the invasiveness of bone biopsy and the specialized expertise required for its widespread use.”

Line 89: "TBS can be performed to evaluate bone quality in OI children" Perhaps instead "TBS can complement standard density measures in assessing skeletal integrity"

Response: According to your suggestion, we have altered the sentence as follows, “These findings possibly imply that TBS can complement standard density measures in assessing skeletal integrity in OI children and adolescents.”

Line 170: How they diagnosed with OI if they do not have a genetic variant? Is it purely a symptomatic diagnosis?

Response: Thank you for your important comment. In the original manuscript, we had included some participants who have been symptomatically diagnosed with OI without confirmation of any pathogenic variants. With such inclusion criteria, some patients with juvenile osteoporosis, which is one of the most important differential diagnoses, were eligible for inclusion in this study. To refine the inclusion criteria, we have included only the participants with OI harboring pathogenic variants and re-analyzed the data in the revised manuscript. We have altered the description regarding the study population as follows. “Between 2015 to 2022, a total of 44 participants who had received a genetic diagnosis of OI underwent DXA at Osaka University, ranging in age from 5 to 20 years. Among them, two participants were excluded from enrollment: one due to the unavailability of a written informed consent form, and another because of the inability to conduct an accurate DXA scan following lumbar spine surgery for scoliosis correction. Thus, overall, 42 participants with OI harboring pathogenic variants were included in this study.

Line 188: Change "affected" to "correlated".

Response: Thank you for your correction. Reviewer 2 raised concerns about the propriety of the multiple regression analysis and proposed that statisticians conduct a statistical review to ascertain the suitability of this analysis. The statisticians concluded that multiple regression analysis with the whole cohort can be misleading and should be deleted from our analysis because some parameters are distributed non-normally. Accordingly, we have removed the multiple regression analysis and its relevant descriptions, including the original line 188.

Line 190: "tended to show" This is overly generous for a correlation of 0.27 and a p value of 0.07. Even if it were significant, it is still a poor correlation.

Response: As mentioned above, we have deleted the description regarding the multiple regression analysis, including the original line 190.

Supplementary Figure 2 is important. Can you bring it to main text? Then you could leave out stats from the paragraph and it will read much easier. Make the BMAD correlation bold in the table as that is the only one that is significant (and negative).

Response: Thank you for your kind suggestion. Reviewer 2 also suggested the result of the original supplementary Figure 2 should appear in the main text. We have added the following sentence just after the description concerning Figure 2. “In addition, after excluding participants with Sillence type Ⅲ to extract non-severe participants, the TBS Z-score was still negatively correlated with annual fracture rate (r = -0.38, p = 0.022, S1 Fig).” The original Supplementary Figure 2 has been re-named Supplementary Figure 1 because the original Supplementary Figure 1 has been changed to Figure 1 as Reviewer 2 had instructed.

Line 190: Why do you think BMADHaz had a positive effect? Isn't that worrying? Wat is the issue with this measure do you think? Discuss in discussion.

Response: We apologize for the confusing results of the multiple regression analysis. Reviewer 2 also raised concerns about this issue and had proposed that statisticians conduct a statistical review to ascertain the suitability of this analysis. The statisticians concluded that multiple regression analysis with the whole cohort can be misleading and should be deleted from our analysis because some parameters are distributed non-normally. Accordingly, we have removed the multiple regression analysis and its relevant descriptions.

Line 192: "We analyzed the relationship of fracture rate with Z-scores." How is this different from line 186 "regression analysis for fracture rate with Z-scores". You need to make it clear that line 186 is the multiple regression analysis and line 192 is the simple linear regression. What does it mean that BMAD has a strong association (multiple regression) but a weak correlation (simple linear regression)?

Response: As described above, we have deleted the multiple regression analysis and its relevant descriptions, which included the sentence that states BMAD has a strong association in multiple regression but a weak correlation in simple linear regression.

Line 225: "usefulness"? Why do you single out BMADHaz? Just say "For the first time we determined correlations between density measures Z-scores of TBS, BMAD, and BMADHaz with fracture risk."

Response: Thank you for your kind suggestion. As you suggested, we have altered the description as follows. “For the first time in existing literature, our study determined the correlations between density measures Z-scores of TBS, BMAD, and BMDHAZ and fracture risk in children and adolescents with OI.”

Line 243: How would you elucidate these? With HRpqCT or pqCT to determine structural deficiencies?

Response: Thank you for your stimulating comment. As you proposed, we agree that research with HRpQCT or pQCT may be able to help elucidate the pathophysiology of OI. Reviewer 2 suggested that the amount of bisphosphonate can also have an impact on these mechanisms, and we have added a related sentence. We then added the following description to reflect your comment. “Further investigations with HRpQCT or pQCT may provide valuable insights into the underlying pathophysiology of OI.”

Line 252: "abnormality of bone size have a strong impact on BMD" This makes it sound like having small bones means you have low BMD. This is an artefact of the imaging technique and using a projection method. This should be made more clear.

Response: As you pointed out, the original description can be misleading. We have altered the description as follows. “Our findings revealed that BMAD correlated with bone fragility only in severe OI, suggesting that an abnormality of bone size, which is corrected by BMAD measurements, can cause inaccuracies in the classical assessment of BMD for these participants.”

Line 253: "BMD itself" It it is adjusted for size, then it isn't BMD, it is BMAD, right?

Response: Thank you for your thorough review. We agree that the original wording can be misleading. We have corrected the description as follows. “Conversely, when the BMAD is applied to adjust for the smallness of bones, the measurement of BMD by DXA can effectively evaluate bone fragility including bone quality in severe cases of OI.”

Discussion: Why do you think that for haploinsufficiency fractures were not correlated with BMAD but glycine fractures were? Whereas haploinsufficiency fractures were correlated with TBS but glycine fractures were not?

Response: Thank you for your important question. We originally wrote that the formula used in BMAD calculation is more appropriate to correct for the bone size effect in severe OI harboring glycine substitution. In addition, Reviewer 2 suggested that it would be useful to consider that a measurement of BMAD results in a stronger correlation with fracture rates due to the mechanical properties of the bone rather than just providing a better estimate of density. Reviewer 2 also commented that small and narrow bones can break more easily than large bones, even if they have the same density, and BMAD might reflect this. We agree with these comments and have added the following descriptions in the discussion section. “Furthermore, it suggests that considering a measurement of BMAD that accounts for bone size leads to a stronger correlation with fracture rates, likely due to the consideration of mechanical properties of the bone.”

Reviewer #2: This study evaluates the recently described Z-scores for several parameters (TBS and BMAD) calculated from retrospective DEXA results in children and adolescents with OI. It compares fracture rates with these measures and the more standard DEXA parameter: height and age-adjusted BMD Z-score. It also analyses the correlation between the Z-scores and fracture rates in mild and severe OI subgroups. There was a negative correlation of fracture rate with TBS in the patients with mild OI and BMAD in patients with severe OI.

Response: Thank you for your review and many insightful comments and suggestions. According to your review, we have modified our manuscript as follows.

Abstract and Introduction:

1. Line 27: the way this is phrased is confusing "The height-for-age Z-score (HAZ)-adjusted BMD-for-age Z-score (BMDHAZ)". I would suggest simplifying it to something like: "the height-adjusted BMD Z-score for age (BMDHAZ)" or the "height-for-age BMD Z-score (BMDHAZ)"

Response: We apologize for the confusing description. According to your suggestion, we rephrased to “the height-adjusted BMD Z-score for age (BMDHAZ)".

2. Line 78: suggest changing the word "professional" to "specialized"

Response: Thank you for your kind suggestion. Accordingly, we have changed the word of “professional” to “specialized” and the corrected sentence is “While the aforementioned findings provide the rationale for performing bone biopsy and HRpQCT routinely in OI assessment of children and adolescents, this approach is difficult to implement given the invasiveness of bone biopsy and the specialized expertise required for its widespread use.”

3. Line 96: suggest changing "bone fragility" to "fracture rate"

Response: According to your suggestion, we have rephrased “bone fragility” to “fracture rate”.

Results:

1. Table 1: this is a little difficult to read. Suggest perhaps breaking up the OI participants into the subgroups you use for the later analysis. Having the number and demographics (average heights, fracture rates and DEXA parameters etc) of the patients with haploinsufficiency vs glycine mutations in this table would be useful.

Response: Thank you for your kind suggestion. Accordingly, we have excluded data of Z-score of height, body weight, BMI, BMDHAZ, BMAD, and TBS, and annual fracture rate from Table 1. Then, we have divided the participants into haploinsufficiency and glycine variant groups and showed the participant number and demographics as Table 2.

2. Although you explain your reasoning for performing both the multiple regression and the simple linear regression (lines 153-155), the conflicting results are confusing. The multiple regression found the BMAD Z-score was significantly negatively correlated, and the BMD-HAZ was significantly positively correlated with fracture rates. However, the simple linear regression found no correlation for either of these DEXA parameters (in the whole cohort). A statistician's input may be helpful here to determine the most appropriate test to present.

Response: Thank you so much for your accurate advice. As you and the Editor recommended, the manuscript underwent a statistical review by statisticians. They pointed out that performing multiple regression analysis with the whole cohort can be misleading and should be deleted from our analysis because some parameters are distributed non-normally. Thus, we have excluded the multiple regression analysis and related descriptions.

Figures:

1. I don't think Figure 1 is required in the main paper, as it is only to prove why you are using these genetic results to represent your "mild" vs "severe" OI phenotype groups. You can simply state the result in the text and include the figure with Figure S3.

Response: We agree with your suggestion. We have described the result regarding the original Figure 1 in the discussion section and have included it in the original Figure S3 (Figure S2 in the revised manuscript).

2. Figure 2 and 3: suggest use "per year" instead of "/yr."

Response: We have altered the description of “ /yr.” to “per year” in all figures.

3. I suggest Figure S1 be included in the main paper (it shows no correlation when the entire cohort is analyzed using simple linear regression) as Fig 2 and 3 (the "mild/haploinsufficiency" and "severe/glycine mutation" subgroups) should be interpreted with this in mind.

Response: Thank you for your kind suggestion. Accordingly, we have included the original Figure S1 in the main paper as Figure 1.

Discussion:

1. Lines 228-229: A result is listed in the discussion that is not mentioned earlier in the paper. Suggest adding this to the results section.

Response: Thank you for your suggestion. Accordingly, we have added a description in the results section as follows: “In addition, after excluding participants with Sillence type Ⅲ to extract non-severe participants, the TBS Z-score was still negatively correlated with annual fracture rate (r = -0.38, p = 0.022, S1 Fig).

2. The postulated explanation for why TBS only correlates in mild OI due to haploinsufficiency is interesting. It would be interesting to know if this correlation simply relates to the amount of bisphosphonate treatment the patient has received (with more severe cases often receiving more treatment) – this is partially addressed later in the discussion

Response: Thank you for your thoughtful suggestion. We agree that it would be interesting to assess whether the correlation simply relates to the amount of BP treatment. We have added the following sentences in the discussion section. “Furthermore, it is essential to clarify whether the dosage of bisphosphonate can impact the correlation between TBS and fracture rate, as patients with more severe bone fragility often receive intensified treatment regimens.”

3. Line 251: suggest changing "evaluated the" to "correlated with"

Response: We have altered the description to “correlated with” in the referred site.

4. Line 257-259: it would be useful to consider that a measure (BMAD) that accounts for the size of the bones results in a stronger correlation with fracture rates due to the mechanical properties of the bone (rather than just providing a better estimate of density). Small/narrow bones break more easily than large bones, even if they have the same density. BMAD might reflect this.

Response: Thank you very much for your important insight. We agree with your suggestion and have added a description regarding this issue as follows, “Furthermore, it suggests that considering a measurement of BMAD that accounts for bone size leads to a stronger correlation with fracture rates, likely due to the consideration of mechanical properties of the bone.”

5. Line 269: missing words "we analyzed fracture rate as a (marker of) severity"

Response: Thank you for your thorough review. We have added the words “marker of” in the referred site.

6. Line 270-273: the difference fracture rates with age are not just due to activity levels. Growth rate and puberty also play a role.

Response: We agree with your suggestion and have revised the description as follows, “The risk of fractures can vary depending on the age of patients, as patient activity level, growth rate, and the stage of puberty are completely different between early childhood and adolescence.”

General comments:

1. Suggest using the wording "children and adolescents with OI" rather than "OI children and adolescents" throughout (e.g. line 86 and 89)

Response: Thank you for your suggestion. We have changed the phrase "OI children and adolescents" to "children and adolescents with OI".

Attachment

Submitted filename: Response_to_Comments_final.docx

Click here for additional data file. (43.3KB, docx)

Decision Letter 1

Heather Macdonald

10 Aug 2023

PONE-D-23-03588R1Association of trabecular bone score and bone mineral apparent density with the severity of bone fragility in children and adolescents with osteogenesis imperfecta: A cross-sectional studyPLOS ONE

Dear Dr. Ozono,

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Academic Editor

PLOS ONE

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Additional Editor Comments:

Thank you for addressing the reviewers' comments. Reviewer 2 has one comment on the Discussion: Page 18 end of paragraph 1: suggest removing “likely due to the consideration of mechanical properties of the bone.” BMAD accounts for bone size but doesn’t specifically account for other mechanical properties of bone.

I have a few other minor comments:

1. In the abstract, start sentence 2 with Areal bone mineral density (BMD). Similarly, on page 5, add areal to the second sentence and remove "the" (Evaluation of areal bone mineral density). Please also remove levels after "lower BMD" in this sentence. Further on in this paragraph, consider remove "of this" after "As a result" and remove the aBMD abbreviation after spine areal BMD.

2. End of pg 5/start of page 6: I find the sentence that begins with "Previously, two studies evaluated..." rather vague. Please reword (i.e., evaluated BMD?). Similarly, the last sentence of this paragraph (that begins with "Although both studies..." could be reworded - perhaps "Although neither study assessed bone fragility, BMD Z-scores and BMAD may be useful approaches to evaluate bone fragility in children and adolescents with OI" (or something like that!).

3. Please change HRpQCT to HR-pQCT throughout the manuscript.

4. Bottom of page 6: By "its widespread use" are the authors referring to HR-pQCT?

5. Discussion, first sentence: "density measures Z-scores of TBS..." could be reworded. In the 2nd sentence, change "risk of fracture" to "fracture rate". Please remove numerical results from the discussion - these should only appear in the results section.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #2: Yes

**********

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Reviewer #2: Yes

**********

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Reviewer #1: All comments have been addressed adequately. The authors are congratulated on an interesting study. Always be careful when using DEXA to assess bones of different size!

Reviewer #2: All comments have been addressed well.

One additional minor comment:

Discussion

Page 18 end of paragraph 1:

I suggest removing “likely due to the consideration of mechanical properties of the bone.”

BMAD accounts for bone size but doesn’t specifically account for other mechanical properties of bone.

**********

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Reviewer #2: No

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PLoS One. 2023 Aug 29;18(8):e0290812. doi: 10.1371/journal.pone.0290812.r004

Author response to Decision Letter 1

16 Aug 2023

RESPONSE TO REVIEWERS

Additional Editor Comments:

Thank you for addressing the reviewers' comments. Reviewer 2 has one comment on the Discussion: Page 18 end of paragraph 1: suggest removing “likely due to the consideration of mechanical properties of the bone.” BMAD accounts for bone size but doesn’t specifically account for other mechanical properties of bone.

Response: Thank you for your thorough review. As the Reviewer 2 has suggested, we have deleted the wording “likely due to the consideration of mechanical properties of the bone.” from the Discussion section.

I have a few other minor comments:

1. In the abstract, start sentence 2 with Areal bone mineral density (BMD). Similarly, on page 5, add areal to the second sentence and remove "the" (Evaluation of areal bone mineral density). Please also remove levels after "lower BMD" in this sentence. Further on in this paragraph, consider remove "of this" after "As a result" and remove the aBMD abbreviation after spine areal BMD.

Response: Per your comments, we have started the sentence 2 with Areal bone mineral density (BMD) in the abstract. On page 5, we have revised the sentence to read “Evaluation of areal bone mineral density.” We have removed the words “levels” and “of this,” as well as the abbreviation “aBMD” from the specified location.

2. End of pg 5/start of page 6: I find the sentence that begins with "Previously, two studies evaluated..." rather vague. Please reword (i.e., evaluated BMD?). Similarly, the last sentence of this paragraph (that begins with "Although both studies..." could be reworded - perhaps "Although neither study assessed bone fragility, BMD Z-scores and BMAD may be useful approaches to evaluate bone fragility in children and adolescents with OI" (or something like that!).

Response: Thank you for your suggestion. To make it clear, we have changed the sentence to “Previously, two studies evaluated BMD in children and adolescents with OI using different parameters.” We also have changed the last sentence of this paragraph as you suggested “Although neither study assessed bone fragility, BMD Z-scores, and BMAD may be useful approaches to evaluate bone fragility in children and adolescents with OI.”

3. Please change HRpQCT to HR-pQCT throughout the manuscript.

Response: We have changed “HRpQCT” to “HR-pQCT” on page 6 and 17.

4. Bottom of page 6: By "its widespread use" are the authors referring to HR-pQCT?

Response: Thank you for your thorough check. We have intended for the term “widespread use” to specifically refer to bone biopsy. To make it clear, we deleted HR-pQCT from this sentence and reworded it as follows, “While the aforementioned findings provide the rationale for performing bone biopsy routinely in OI assessment of children and adolescents, this approach is difficult to implement given the invasiveness of bone biopsy and the specialized expertise required for its widespread use.”

5. Discussion, first sentence: “density measures Z-scores of TBS…” could be reworded. In the 2nd sentence, change “risk of fracture” to “fracture rate”. Please remove numerical results from the discussion – these should only appear in the results section.

Response: Thank you for your advice. Following your suggestion, we have made two changes: firstly, the sentence now reads, “For the first time in existing literature, our study determined the correlations between Z-scores of TBS, BMAD, and BMDHAZ and fracture risk in children and adolescents with OI.” Secondly, we have replaced “risk of fracture” with “fracture rate.” We have deleted the description of numerical results from the discussion section.

Reviewers' comments:

Reviewer #1: All comments have been addressed adequately. The authors are congratulated on an interesting study. Always be careful when using DEXA to assess bones of different size!

Response: Your careful and professional review greatly enhanced our manuscript. We deeply appreciate your invaluable advice and guidance.

Reviewer #2: All comments have been addressed well.

One additional minor comment:

Discussion

Page 18 end of paragraph 1:

I suggest removing “likely due to the consideration of mechanical properties of the bone.”

BMAD accounts for bone size but doesn’t specifically account for other mechanical properties of bone.

Response: Thank you for your suggestion. We have deleted the wording “likely due to the consideration of mechanical properties of the bone.” from the Discussion section. Your expert review has dramatically improved our manuscript. We sincerely appreciate your invaluable guidance.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (17.2KB, docx)

Decision Letter 2

Heather Macdonald

17 Aug 2023

Association of trabecular bone score and bone mineral apparent density with the severity of bone fragility in children and adolescents with osteogenesis imperfecta: A cross-sectional study

PONE-D-23-03588R2

Dear Dr. Ozono,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Heather Macdonald, Ph.D

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Thank you for addressing the 2nd round of comments.

Reviewers' comments:

Acceptance letter

Heather Macdonald

22 Aug 2023

PONE-D-23-03588R2

Association of trabecular bone score and bone mineral apparent density with the severity of bone fragility in children and adolescents with osteogenesis imperfecta: A cross-sectional study

Dear Dr. Ozono:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Heather Macdonald

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Detailed data of the study participants.

    Age, age at the DXA scan; FR, the number of fractures up to the DXA scan divided by the age; BMDHAZ, height-for-age Z-score-adjusted bone mineral density-for-age Z-score; BMAD, Z-score of bone mineral apparent density; TBS, Z-score of trabecular bone score; Ht-SD, standard deviation score of height; BW-SD, standard deviation score of body weight; Tx, treatment at the DXA scan; past Tx, past treatment history; RIS, risedronate, PAM, pamidronate; ALN, alendronate; ZOL, zoledronic acid; Elde, eldecalcitol; Alfa, alfacalcidol; MSCT, mesenchymal stem cell transplantation in utero; none, no treatment; n.d., not detected; † We previously confirmed this deletion variant causing exon 21 skipping (p.Gly364_Arg399del) by mRNA analysis (Takeyari S, Kubota T, Ohata Y, Fujiwara M, Kitaoka T, Taga Y, et al. 4-Phenylbutyric acid enhances the mineralization of osteogenesis imperfecta iPSC-derived osteoblasts. J Biol Chem. 2021;296:100027. Epub 20201123. doi: 10.1074/jbc.RA120.014709. PubMed PMID: 33154166; PubMed Central PMCID: PMC7948972.).

    (DOCX)

    Click here for additional data file. (28.4KB, docx)
    S1 Fig. Correlation between annual fracture rate (FR) and Z-scores of TBS, BMAD, and BMDHAZ in non-severe OI participants without individuals with Sillence type Ⅲ (n = 37).

    (TIF)

    Click here for additional data file. (206.8KB, tif)
    S2 Fig. Comparison of the annual fracture rate and height Z-score (Ht-SD) between OI participants harboring nonsense and frameshift variants in COL1A1 causing haploinsufficient defect (HI, n = 17) and glycine substitution (GS) either in COL1A1 or COL1A2 causing severe phenotype (n = 9).

    (TIF)

    Click here for additional data file. (120KB, tif)
    S3 Fig. Correlation between Z-scores of BMAD and BMDHAZ in all participants (n = 42) and in individuals with glycine substitution either in COL1A1 or COL1A2 (n = 9).

    (TIF)

    Click here for additional data file. (157.6KB, tif)
    Attachment

    Submitted filename: Response_to_Comments_final.docx

    Click here for additional data file. (43.3KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (17.2KB, docx)

    Data Availability Statement

    All relevant data are within the paper and its Supporting Information files (S1 Table).

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