confirmed through reduced collagen maturity and altered
carbonate-to-phosphate ratio using FTIR (40).

## 4.5 Immunohistochemistry of Bone (IHC)

Immunohistochemistry (IHC) allows to determine the cellular localization of proteins and their expression within tissues (228). This technique requires two phases: 1) specimen fixation and tissue processing and 2) interpretation and quantification of the obtained expression (229). Tissue properties can be analysed in depth, making it possible to study not only bone but also the surrounding tissues like cartilage, muscle and tendons. There are different approaches in immunohistochemistry analysis and reporting and for some IHC markers like bone morphogenic proteins (BMP), osteocalcin (OCN), osteopontin (OPN), and few others scoring systems are available (230), which may include IHC markers of the surrounding tissue. Sequential antibody immunostaining for quantification is used to detect antigens of interest. This is a complementary method to *in situ* hybridization histochemistry (ISHH) which detects cellular nucleic acids based on the formation of double-stranded hybrids between a nucleic acid fragment (the probe) and a DNA or RNA sequence present within bone cells (231).

**Limitations:** Decalcified bone samples are widely used for IHC. However, during the decalcification process, the integrity of the trabecular network is lost, which can cause changes in the overall appearance of the bone morphology. Careful specimen preparation and analysis by experienced researchers should be applied. As an alternative, methyl methacrylate embedding retains the mineral fraction of the bone tissue. However, the hard embedding makes sectioning more difficult and epitope retrieval complex (228). The process of optimization of the method for each target can be relatively time-consuming, costly and labour intense.

**Strengths:** For humans, a great variety of antibodies are available and established for IHC. On its own and in combination with different antibodies and other staining techniques, this enables a multitude of possibilities to visualize complex interactions.

In mouse studies, IHC can provide useful information on the
temporo-spatial expression of key factors important for
musculoskeletal development and function. However, careful and
experienced specimen handling of the small mouse samples is
needed due to the tendency for samples to detach from slides.
IHC has been used to locate many important antigens in bone such
as SOX9, OSX and sclerostin (232, 233). As in humans, decalcified
bone specimens are most commonly used.

Immunohistochemistry is applied in histological sections (234) and whole-mount zebrafish samples, often performed in larval stages and dissected adult tissues (171, 235-237). While a plethora of antibodies are available for human and mice proteins, only few are available for zebrafish. Antibody tests and protocol optimization need to be performed in zebrafish as for mice and human samples.

## 5 BIOCHEMISTRY FOR BONE PHENOTYPING

In patients, a number of general laboratory analyses are necessary to
assess a patient's general health and potential causes of secondary

bone disease (238, 239). These include a full blood count, erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP), markers of liver and kidney function and markers of calcium/ phosphate metabolism. Further, optional tests include serum proteins (including electrophoresis; to exclude multiple myeloma), markers of thyroid function (to exclude thyrotoxicosis), sex hormones (to exclude hypogonadism) and measurement of free cortisol in 24-hour urine for screening for Cushing's syndrome. Some additional tests might be useful to exclude other pathologies, e.g. celiac disease via transglutaminase antibodies or systemic mastocytosis via serum tryptase and/or urine methyl histamine.

**Limitations:** Optional laboratory parameters require a diagnostic plan for the individual patient to be useful and may be more expensive.

**Strengths:** Differential diagnoses are frequently based on the knowledge about general health conditions and should be available in acceptable quality.

In mice, most studies use a defined mouse model for distinct research questions. Therefore, it is not necessary to perform biochemistry for the diagnosis of a disease in this context. However, biochemistry can be performed on blood and plasma samples. Other than in humans, the amount of blood obtained from the living animal can be a limiting factor for such tests. They are therefore mostly performed with terminal blood collection in mice.

In zebrafish, although neither pregnancy nor lactation exist, the sex-hormonal changes and sexual-development milestones (analogous to “puberty” or “post-reproductive” age) are well characterized. Sex in zebrafish (as well as amphibians and reptiles) is not determined by a particular chromosome, but by the interaction between gene and environment. Sex hormones are well studied, hypogonadism could be easily obtained and well as orchid- and ovariectomy. Catecholamines, mineralocorticoids and microelements are measured similarly to mammals, as well as thyroid stimulating hormone and its receptor (13). Blood can be collected from the adult zebrafish through the aorta and decapitation. Recently, it has been shown that repeated blood collection can be performed from the same adult zebrafish longitudinally for the measurements of triglycerides and glucose (240). Due to the small size of zebrafish, a limitation of the method is the total blood sample volume that can be collected at time, ≤0.4% of body weight per week for repeated measurements.

## 5.1 Controllers of Bone Mass and Mineralisation

Many factors are involved in the regulation of bone mineralization, among them calcium, phosphate, calcitriol, fibroblast growth factor 23 (FGF23) and parathyroid hormone (PTH). A major clinical problem worldwide is vitamin D deficiency, which has attracted considerable interest over the last two decades (241). Active Vitamin D is the main supplier of bone minerals to bone tissue. In patients with sufficient vitamin D levels, additional supplementation has no effect on bone (242). Hence, recent studies in osteoporosis have seriously questioned the routine use of vitamin D supplementation for the purpose of preventing osteoporotic fractures (243). Bone mineralization disorders in general (osteomalacia/rickets) can easily be excluded, diagnosed
confirmed through reduced collagen maturity and altered
carbonate-to-phosphate ratio using FTIR (40).

## 4.5 Immunohistochemistry of Bone (IHC)

Immunohistochemistry (IHC) allows to determine the cellular localization of proteins and their expression within tissues (228). This technique requires two phases: 1) specimen fixation and tissue processing and 2) interpretation and quantification of the obtained expression (229). Tissue properties can be analysed in depth, making it possible to study not only bone but also the surrounding tissues like cartilage, muscle and tendons. There are different approaches in immunohistochemistry analysis and reporting and for some IHC markers like bone morphogenic proteins (BMP), osteocalcin (OCN), osteopontin (OPN), and few others scoring systems are available (230), which may include IHC markers of the surrounding tissue. Sequential antibody immunostaining for quantification is used to detect antigens of interest. This is a complementary method to *in situ* hybridization histochemistry (ISHH) which detects cellular nucleic acids based on the formation of double-stranded hybrids between a nucleic acid fragment (the probe) and a DNA or RNA sequence present within bone cells (231).

**Limitations:** Decalcified bone samples are widely used for IHC. However, during the decalcification process, the integrity of the trabecular network is lost, which can cause changes in the overall appearance of the bone morphology. Careful specimen preparation and analysis by experienced researchers should be applied. As an alternative, methyl methacrylate embedding retains the mineral fraction of the bone tissue. However, the hard embedding makes sectioning more difficult and epitope retrieval complex (228). The process of optimization of the method for each target can be relatively time-consuming, costly and labour intense.

**Strengths:** For humans, a great variety of antibodies are available and established for IHC. On its own and in combination with different antibodies and other staining techniques, this enables a multitude of possibilities to visualize complex interactions.

In mouse studies, IHC can provide useful information on the
temporo-spatial expression of key factors important for
musculoskeletal development and function. However, careful and
experienced specimen handling of the small mouse samples is
needed due to the tendency for samples to detach from slides.
IHC has been used to locate many important antigens in bone such
as SOX9, OSX and sclerostin (232, 233). As in humans, decalcified
bone specimens are most commonly used.

Immunohistochemistry is applied in histological sections (234) and whole-mount zebrafish samples, often performed in larval stages and dissected adult tissues (171, 235-237). While a plethora of antibodies are available for human and mice proteins, only few are available for zebrafish. Antibody tests and protocol optimization need to be performed in zebrafish as for mice and human samples.

## 5 BIOCHEMISTRY FOR BONE PHENOTYPING

In patients, a number of general laboratory analyses are necessary to
assess a patient's general health and potential causes of secondary

bone disease (238, 239). These include a full blood count, erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP), markers of liver and kidney function and markers of calcium/ phosphate metabolism. Further, optional tests include serum proteins (including electrophoresis; to exclude multiple myeloma), markers of thyroid function (to exclude thyrotoxicosis), sex hormones (to exclude hypogonadism) and measurement of free cortisol in 24-hour urine for screening for Cushing's syndrome. Some additional tests might be useful to exclude other pathologies, e.g. celiac disease via transglutaminase antibodies or systemic mastocytosis via serum tryptase and/or urine methyl histamine.

**Limitations:** Optional laboratory parameters require a diagnostic plan for the individual patient to be useful and may be more expensive.

**Strengths:** Differential diagnoses are frequently based on the knowledge about general health conditions and should be available in acceptable quality.

In mice, most studies use a defined mouse model for distinct research questions. Therefore, it is not necessary to perform biochemistry for the diagnosis of a disease in this context. However, biochemistry can be performed on blood and plasma samples. Other than in humans, the amount of blood obtained from the living animal can be a limiting factor for such tests. They are therefore mostly performed with terminal blood collection in mice.

In zebrafish, although neither pregnancy nor lactation exist, the sex-hormonal changes and sexual-development milestones (analogous to “puberty” or “post-reproductive” age) are well characterized. Sex in zebrafish (as well as amphibians and reptiles) is not determined by a particular chromosome, but by the interaction between gene and environment. Sex hormones are well studied, hypogonadism could be easily obtained and well as orchid- and ovariectomy. Catecholamines, mineralocorticoids and microelements are measured similarly to mammals, as well as thyroid stimulating hormone and its receptor (13). Blood can be collected from the adult zebrafish through the aorta and decapitation. Recently, it has been shown that repeated blood collection can be performed from the same adult zebrafish longitudinally for the measurements of triglycerides and glucose (240). Due to the small size of zebrafish, a limitation of the method is the total blood sample volume that can be collected at time, ≤0.4% of body weight per week for repeated measurements.

## 5.1 Controllers of Bone Mass and Mineralisation

Many factors are involved in the regulation of bone mineralization, among them calcium, phosphate, calcitriol, fibroblast growth factor 23 (FGF23) and parathyroid hormone (PTH). A major clinical problem worldwide is vitamin D deficiency, which has attracted considerable interest over the last two decades (241). Active Vitamin D is the main supplier of bone minerals to bone tissue. In patients with sufficient vitamin D levels, additional supplementation has no effect on bone (242). Hence, recent studies in osteoporosis have seriously questioned the routine use of vitamin D supplementation for the purpose of preventing osteoporotic fractures (243). Bone mineralization disorders in general (osteomalacia/rickets) can easily be excluded, diagnosed
and treated - there is global consensus for what constitutes sufficient vitamin D levels and calcium intake for the prevention of osteomalacia and rickets (244).

The calcium sensing receptor senses decreased dietary calcium supply and increases PTH secretion. Hence, PTH levels are inversely correlated with 25-hydroxycholecalciferol [25(OH)D] levels and dietary calcium supply. However, while PTH is generally known as an indicator of vitamin D status, there is no consensus regarding the accuracy of measuring PTH to determine vitamin D depletion (245). Dietary calcium supply is the likely reason for this (245). Through an unknown phosphate sensing mechanism, FGF23 controls renal phosphate reabsorption. Bone hypo-mineralisation (osteomalacia, rickets) only develops when serum phosphate is low (246) and is accompanied by elevated (bone) alkaline phosphatase and PTH concentrations.

Laboratory analysis of calciotropic hormones, mainly 25-hydroxycholecalciferol (25(OH)D) as a surrogate for the individual pool of vitamin D and several metabolites including the active 1,25-dihydroxycalciferol (1,25(OH2)D) have gradually undergone worldwide standardization with European and U.S. quality and accuracy methods including reference samples used as gold standards for both mass spectrometry and enzyme-linked assays (247). PTH measurements have undergone considerable development over the years, discriminating between the entire molecule and distinct fragments. Currently used assays identify intact PTH and assays for subforms are still available for specific questions.

**Limitations:** There is an urgent need to define non-invasive diagnostic criteria for osteomalacia (248). There is some discussion about the analytical approaches and the conversion of units used e.g. in 25(OH)D measurements. There is no single biochemical marker that represents normal bone mineralization.

**Strengths:** Bone mineralization disorders can be excluded by simple blood tests.

In mice, assays are available for testing serum levels of vitamin D and PTH in mice. Although these tests have been used in the study of dietary intervention and bone health, they are not regularly used in the routine monitoring of bone health in mice (249). On the other hand, a particular benefit of mouse models for studying skeletal disease is that they have less natural variation than humans. Identical diets, environments and genetics mean that mice should have minimal variation in vitamin D or PTH levels.

In zebrafish, measurements have not yet been performed *in vivo* in longitudinal studies. Upon fish decapitation, blood and serum readings could be potentially performed for levels of PTH, calcium phosphate (dependent on food intake), FGF23 levels and vitamin D. It would be interesting to test if such assays could be performed using small volumes of blood/serum that can be collected from zebrafish allowing longitudinal studies.

## 5.2 Bone Turnover Markers (BTMs)

Systemic markers of bone turnover (BTMs) reflect bone remodelling in adults (250), but also a combination of bone remodelling, modelling and 3-dimensional bone growth in children (251) (Figure 3).

Bone formation markers such as osteocalcin (OC), and N-terminal and C-terminal propeptides of type I procollagen (PINP, PICP) are proteins secreted by osteoblasts and represent the activity of bone formation. PINP is also expressed in other tissues (e.g., skin) and during fibrotic processes. Therefore, its concentration may be elevated in skin diseases and in case of active fibrosis (e.g., liver, lungs, heart) (252). Bone alkaline phosphatase (bone ALP) is an ectoenzyme present on the outer surface of osteoblasts. Their serum levels are correlated positively with histomorphometric measures of bone formation (e.g., osteoid surface, appositional rate, mineralization) (253).

The markers of bone resorption activity comprise C-terminal and N-terminal telopeptides of type I collagen (CTX, NTX), deoxypyridinoline (DPD) and hydroxyproline (HPro). They are products of bone collagen degradation. Blood and urinary levels of bone resorption markers reflect the activity of bone resorption. They are correlated positively with histomorphometric measures of bone resorption and decrease rapidly after administration of an anti-resorptive agent (254). Among bone resorption markers, CTX is the most specific for bone. Tartrate-resistant acid phosphatase 5b (TRAP5b) is an enzyme expressed by osteoclasts. It is an indicator of the presence (number) of osteoclasts, but not necessarily of their resorptive activity. Therefore, in some situations (osteopetrosis, treatment with cathepsin K inhibitors), discrepancy between the TRAP5b concentration and the levels of collagen degradation products may be observed (255, 256).

Serum PINP and CTX are defined as the reference markers of bone formation and bone resorption (257).

**Limitations:** Their specific technical and analytical limitations are categorized in two main groups (258):

**Analytical variability:** Despite the reduced analytical coefficient of variation (CV) of the techniques used to measure these markers, lack of a uniform standardization technique has resulted in difficulties in comparing values obtained by dissimilar methods in different laboratories. The intra-individual biological variability of BTMs is still of concern, especially when therapeutic approaches should be chosen based on a single measurement.

**Pre-analytical variability:** The BTMs' preanalytical variability due to uncontrollable and controllable factors should be considered during their clinical interpretation. Uncontrollable factors are for instance age, sex, renal function, growth rate, pubertal or menopausal status, comorbidities or recent fracture. The use of appropriate reference ranges, in particular in children, and suitable adjustments can help overcome this variability to some extent. Controllable factors are for instance food intake/ fasting status, circadian and menstrual cycle or exercise. The effects of these factors can be minimized by standardizing the timing and conditions of sample collection.

Measurements of BTMs are not helpful for diagnosis of osteoporosis, bone fragility or skeletal dysplasia. For instance, only 20% of osteoporotic women had serum CTX-I concentration exceeding the upper limit of the reference values in premenopausal women (259). However, elevated BTM levels (especially urinary bone resorption markers and bone ALP) may point to more rapid bone loss and higher risk of hip fracture in adults, mainly in postmenopausal women (260, 261). On an individual level, BTMs may be helpful for monitoring
and treated - there is global consensus for what constitutes sufficient vitamin D levels and calcium intake for the prevention of osteomalacia and rickets (244).

The calcium sensing receptor senses decreased dietary calcium supply and increases PTH secretion. Hence, PTH levels are inversely correlated with 25-hydroxycholecalciferol [25(OH)D] levels and dietary calcium supply. However, while PTH is generally known as an indicator of vitamin D status, there is no consensus regarding the accuracy of measuring PTH to determine vitamin D depletion (245). Dietary calcium supply is the likely reason for this (245). Through an unknown phosphate sensing mechanism, FGF23 controls renal phosphate reabsorption. Bone hypo-mineralisation (osteomalacia, rickets) only develops when serum phosphate is low (246) and is accompanied by elevated (bone) alkaline phosphatase and PTH concentrations.

Laboratory analysis of calciotropic hormones, mainly 25-hydroxycholecalciferol (25(OH)D) as a surrogate for the individual pool of vitamin D and several metabolites including the active 1,25-dihydroxycalciferol (1,25(OH2)D) have gradually undergone worldwide standardization with European and U.S. quality and accuracy methods including reference samples used as gold standards for both mass spectrometry and enzyme-linked assays (247). PTH measurements have undergone considerable development over the years, discriminating between the entire molecule and distinct fragments. Currently used assays identify intact PTH and assays for subforms are still available for specific questions.

**Limitations:** There is an urgent need to define non-invasive diagnostic criteria for osteomalacia (248). There is some discussion about the analytical approaches and the conversion of units used e.g. in 25(OH)D measurements. There is no single biochemical marker that represents normal bone mineralization.

**Strengths:** Bone mineralization disorders can be excluded by simple blood tests.

In mice, assays are available for testing serum levels of vitamin D and PTH in mice. Although these tests have been used in the study of dietary intervention and bone health, they are not regularly used in the routine monitoring of bone health in mice (249). On the other hand, a particular benefit of mouse models for studying skeletal disease is that they have less natural variation than humans. Identical diets, environments and genetics mean that mice should have minimal variation in vitamin D or PTH levels.

In zebrafish, measurements have not yet been performed *in vivo* in longitudinal studies. Upon fish decapitation, blood and serum readings could be potentially performed for levels of PTH, calcium phosphate (dependent on food intake), FGF23 levels and vitamin D. It would be interesting to test if such assays could be performed using small volumes of blood/serum that can be collected from zebrafish allowing longitudinal studies.

## 5.2 Bone Turnover Markers (BTMs)

Systemic markers of bone turnover (BTMs) reflect bone remodelling in adults (250), but also a combination of bone remodelling, modelling and 3-dimensional bone growth in children (251) (Figure 3).

Bone formation markers such as osteocalcin (OC), and N-terminal and C-terminal propeptides of type I procollagen (PINP, PICP) are proteins secreted by osteoblasts and represent the activity of bone formation. PINP is also expressed in other tissues (e.g., skin) and during fibrotic processes. Therefore, its concentration may be elevated in skin diseases and in case of active fibrosis (e.g., liver, lungs, heart) (252). Bone alkaline phosphatase (bone ALP) is an ectoenzyme present on the outer surface of osteoblasts. Their serum levels are correlated positively with histomorphometric measures of bone formation (e.g., osteoid surface, appositional rate, mineralization) (253).

The markers of bone resorption activity comprise C-terminal and N-terminal telopeptides of type I collagen (CTX, NTX), deoxypyridinoline (DPD) and hydroxyproline (HPro). They are products of bone collagen degradation. Blood and urinary levels of bone resorption markers reflect the activity of bone resorption. They are correlated positively with histomorphometric measures of bone resorption and decrease rapidly after administration of an anti-resorptive agent (254). Among bone resorption markers, CTX is the most specific for bone. Tartrate-resistant acid phosphatase 5b (TRAP5b) is an enzyme expressed by osteoclasts. It is an indicator of the presence (number) of osteoclasts, but not necessarily of their resorptive activity. Therefore, in some situations (osteopetrosis, treatment with cathepsin K inhibitors), discrepancy between the TRAP5b concentration and the levels of collagen degradation products may be observed (255, 256).

Serum PINP and CTX are defined as the reference markers of bone formation and bone resorption (257).

**Limitations:** Their specific technical and analytical limitations are categorized in two main groups (258):

**Analytical variability:** Despite the reduced analytical coefficient of variation (CV) of the techniques used to measure these markers, lack of a uniform standardization technique has resulted in difficulties in comparing values obtained by dissimilar methods in different laboratories. The intra-individual biological variability of BTMs is still of concern, especially when therapeutic approaches should be chosen based on a single measurement.

**Pre-analytical variability:** The BTMs' preanalytical variability due to uncontrollable and controllable factors should be considered during their clinical interpretation. Uncontrollable factors are for instance age, sex, renal function, growth rate, pubertal or menopausal status, comorbidities or recent fracture. The use of appropriate reference ranges, in particular in children, and suitable adjustments can help overcome this variability to some extent. Controllable factors are for instance food intake/ fasting status, circadian and menstrual cycle or exercise. The effects of these factors can be minimized by standardizing the timing and conditions of sample collection.

Measurements of BTMs are not helpful for diagnosis of osteoporosis, bone fragility or skeletal dysplasia. For instance, only 20% of osteoporotic women had serum CTX-I concentration exceeding the upper limit of the reference values in premenopausal women (259). However, elevated BTM levels (especially urinary bone resorption markers and bone ALP) may point to more rapid bone loss and higher risk of hip fracture in adults, mainly in postmenopausal women (260, 261). On an individual level, BTMs may be helpful for monitoring
<figure><img src="image_6.png" /><figcaption>**FIGURE 3** | Bone turnover markers and players in bone turnover/mass balance. bALP; bone alkaline phosphatase, Ca²⁺; calcium/ionised calcium, CTX; C-terminal telopeptide of type I collagen, DPD; deoxypyridinoline, FGF23; fibroblast growth factor 23, HPro; hydroxyproline, PINP and PICP; N-terminal and C-terminal propeptides of type I procollagen, PO₄²⁺; phosphate, PTH; parathyroid hormone, TRAP5b; tartrate-specific acid phosphatase type 5b.</figcaption></figure>

anti-osteoporotic treatments in adults (262) and children (263). Circadian rhythms and fasting are important factors in the clinical validation - blood samples for PINP can be collected at any time of the day due to only a slight circadian variation comparable to the measurement error of the assay (264). By contrast, blood CTX decreases rapidly after breakfast (265) and blood for CTX assay must be collected in the fasting status in the morning before 10 am.

In children, specific age- and development-associated reference ranges are used for most of the markers but the diagnostic value is limited (266).

Strengths: BTMs are non-invasive to bone. They closely monitor systemic bone turnover and are increasingly established and accessible. Standardization of the analytes is ongoing, but assay results may differ between providers.

BTMs are easily measured in mice using ELISA or commercially available assays. In an ovariectomized mouse (as a model of osteoporosis) OC and CTX provided conclusive outcomes on bone turnover when compared to µCT imaging (267, 268). Although serum BTMs hold the benefit of allowing multiple tests to be taken from one animal over a time course, it should be considered when planning an experiment whether more useful data can be gained by qPCR or western blot of bone tissue post mortem.

Bone formation (osteocalcin, alkaline phosphatase) and resorption markers (TRAP) are measured *ex vivo* through immunoassays in whole-mount, dissected tissues and on histological sections. For *in vivo* studies Alizarin Red and Calcein can be used to measure bone formation as well as the use of transgenic lines for osteocalcin, and TRAP line labelling osteoclasts (205, 269). *In vivo* longitudinal studies for such markers through blood and serum collection have not yet been performed in zebrafish.

## 6 NEW PHENOTYPING AND FUTURE ASPECTS

For the purpose of personalized medicine and to avoid a “one size fits all” approach, a differentiated pattern of patients’ characteristics might be a goal for future investigations based on age, sex and ethnicity and other background information, which should be taken into consideration. Duration of exposure to any harmful agent or environmental condition is important during the life course and of great interest from the epigenetic perspective. In the young, damaging environmental exposures have not yet accumulated. One example is obesity, where
<figure><img src="image_6.png" /><figcaption>**FIGURE 3** | Bone turnover markers and players in bone turnover/mass balance. bALP; bone alkaline phosphatase, Ca²⁺; calcium/ionised calcium, CTX; C-terminal telopeptide of type I collagen, DPD; deoxypyridinoline, FGF23; fibroblast growth factor 23, HPro; hydroxyproline, PINP and PICP; N-terminal and C-terminal propeptides of type I procollagen, PO₄²⁺; phosphate, PTH; parathyroid hormone, TRAP5b; tartrate-specific acid phosphatase type 5b.</figcaption></figure>

anti-osteoporotic treatments in adults (262) and children (263). Circadian rhythms and fasting are important factors in the clinical validation - blood samples for PINP can be collected at any time of the day due to only a slight circadian variation comparable to the measurement error of the assay (264). By contrast, blood CTX decreases rapidly after breakfast (265) and blood for CTX assay must be collected in the fasting status in the morning before 10 am.

In children, specific age- and development-associated reference ranges are used for most of the markers but the diagnostic value is limited (266).

Strengths: BTMs are non-invasive to bone. They closely monitor systemic bone turnover and are increasingly established and accessible. Standardization of the analytes is ongoing, but assay results may differ between providers.

BTMs are easily measured in mice using ELISA or commercially available assays. In an ovariectomized mouse (as a model of osteoporosis) OC and CTX provided conclusive outcomes on bone turnover when compared to µCT imaging (267, 268). Although serum BTMs hold the benefit of allowing multiple tests to be taken from one animal over a time course, it should be considered when planning an experiment whether more useful data can be gained by qPCR or western blot of bone tissue post mortem.

Bone formation (osteocalcin, alkaline phosphatase) and resorption markers (TRAP) are measured *ex vivo* through immunoassays in whole-mount, dissected tissues and on histological sections. For *in vivo* studies Alizarin Red and Calcein can be used to measure bone formation as well as the use of transgenic lines for osteocalcin, and TRAP line labelling osteoclasts (205, 269). *In vivo* longitudinal studies for such markers through blood and serum collection have not yet been performed in zebrafish.

## 6 NEW PHENOTYPING AND FUTURE ASPECTS

For the purpose of personalized medicine and to avoid a “one size fits all” approach, a differentiated pattern of patients’ characteristics might be a goal for future investigations based on age, sex and ethnicity and other background information, which should be taken into consideration. Duration of exposure to any harmful agent or environmental condition is important during the life course and of great interest from the epigenetic perspective. In the young, damaging environmental exposures have not yet accumulated. One example is obesity, where
duration of exposure in relation to bone health is something comparatively little is known, but which could be explored in animal models.

There are numerous important new angles, e.g. the view on bone health early in life, shown by the recent insights in the paediatric field. Children with severe illness or skeletal disorders often have short stature, scoliosis, joint contractures and bone deformities. Hence, despite size adjustment, DXA data can be falsely low or unobtainable. Therefore, clinical assessment of bone health and research studies focus on defining bone phenotypes from bone biopsy, x-rays and vertebral fractures and less on DXA. This is improving our understanding of bone physiology of rare diseases. For example, skeletal effects associated with transgender pharmacotherapy have not yet been widely studied. Issues include start-time for treatment, particularly with regard to puberty, and the short- and long-term effects from cross-sex therapies on mineral metabolism.

Looking to the future, a more holistic approach to musculoskeletal health is needed given the multifactorial, polygenic nature of osteoporosis, to facilitate healthy aging/frailty prevention.

## 6.1 Future Developments in 2D and 3D Imaging

To date, bone mass measured by DXA is the most widely used bone phenotypic measure for genetic population studies of osteoporosis, in large part due to the ubiquitous presence of DXA scanners, which are widely used clinically and in large scale population studies. More recent genetic studies have examined other related phenotypes including hip geometry and shape (270). Looking to the future, DXA scans are likely to contribute to genetic studies of other age-related musculoskeletal conditions. For example, a range of other phenotypes, more closely related to osteoarthritis, are currently being generated in hip and knee DXA scans from approximately 100,000 individuals from UK Biobank<fnref n="4" />. However, a broad number of new technical approaches are under development.

For mammalian and fish studies, an optical coherence tomography (OCT) and synchrotron radiation microcomputed tomography (SR-µCT) may be developed for future use. OCT provides non-invasive high-resolution three-dimensional (3D) images of biological tissue and quantification of chromophores in tissues (271). Fish and human bones contain hydroxyapatite crystals so it can be compared and quantification of BMD is possible to obtain by comparison to sample with known hydroxyapatite levels. Better resolution (100 nm) can be achieved with synchrotron equipped µCT technologies (SR-µCT) with better assessing of bone micro-architecture (11).

Among recently developed MRI techniques is ultrashort echo time (UTE) MRI. Clinical MRI cannot detect water bound to organic matrix, or the free water in the pores of the Haversian system of cortical bone due to the very short apparent transverse relaxation times (T2 *). Therefore, a new class of sequences, ultrashort-TE (UTE) sequences have been developed recently, with TEs of less than 100 µs. This is much shorter than TEs of

conventional sequences. These sequences can be used to detect water signals from within cortical bone (272, 273).

PET scanning can be used for some analyses in bone, and has been used in animal models to assess changes in e.g. bone vascularity or stress fractures (274). However, the reduction of radiation and dose exposures are critical in medical and research imaging, since high doses of radiation are associated with DNA damage. Multiple researchers are actively engaged in the development of clinical total-body PET hardware, promising improvements in dose reductions, reduced scan times, and quantitative kinetic modelling capabilities (275).

Artificial intelligence as new methods for automatic image segmentation, and prediction of fracture risk shows promising clinical value (276). Advances in artificial intelligence (deep learning) also perform well in classifying skeletal radiographs (277). Drug and genetic screening as well as longitudinal studies in zebrafish would benefit from AI, towards implementing novel platforms for gene functional validation through rapid skeletal phenotypic assessment.

## 6.2 Contribution of -omics Technologies to the Phenotypic Dissection of Musculoskeletal Traits

Historically, genome-wide association studies (GWAS) and next generation sequencing have revolutionized genetic diagnostic services and our understanding of common and rare bone diseases. For example, there are now 20+ genes identified that cause osteogenesis imperfecta through whole genome, exome and RNA sequencing and thousands of genetic variants arising from meta-analyses of the Genetic Factors of Osteoporosis (GEFOS) consortium, and the UK Biobank.

Nevertheless, several steps are needed before GWAS discoveries can be translated to biologic processes underlying the genotype-phenotype relationship. First, variants identified by GWAS need to be linked to the gene(s) in the region. Second, such target genes [identified through GWAS or whole exome sequencing (WES)] need to be placed in the context of pathways affected by the genetic variation. Third, functional, mechanistic studies need to establish how the given alteration of the biologic pathway(s) results in a phenotype.

It is here, where the confluence of a roadmap of gene functional evaluations and a detailed assessment of the laboratory and musculoskeletal phenotype can provide mechanistic insight into the relevant biological processes underlying disease. Multi-omics approaches are used to identify laboratory, phenotypic, disease-specific signatures.

Here we provide a succinct overview of multi-omics layers and their importance for bone phenotyping:

**Genomics and Epigenomics:** Gene coding regions (underlying most Mendelian disorders) make up less than 3% of the human genome while, approximately 90% of the single-nucleotide polymorphisms (SNPs) that are associated with human disease lie within intergenic or intronic regions. As such, genetic variation (polymorphisms, insertion/deletions and mutations) in intergenic regions, such as enhancers, can strongly affect gene expression, demonstrating a tight regulatory network between the coding and noncoding parts of the genome.

<footnote n="4">⁴https://www.ukbiobank.ac.uk/.</footnote>
duration of exposure in relation to bone health is something comparatively little is known, but which could be explored in animal models.

There are numerous important new angles, e.g. the view on bone health early in life, shown by the recent insights in the paediatric field. Children with severe illness or skeletal disorders often have short stature, scoliosis, joint contractures and bone deformities. Hence, despite size adjustment, DXA data can be falsely low or unobtainable. Therefore, clinical assessment of bone health and research studies focus on defining bone phenotypes from bone biopsy, x-rays and vertebral fractures and less on DXA. This is improving our understanding of bone physiology of rare diseases. For example, skeletal effects associated with transgender pharmacotherapy have not yet been widely studied. Issues include start-time for treatment, particularly with regard to puberty, and the short- and long-term effects from cross-sex therapies on mineral metabolism.

Looking to the future, a more holistic approach to musculoskeletal health is needed given the multifactorial, polygenic nature of osteoporosis, to facilitate healthy aging/frailty prevention.

## 6.1 Future Developments in 2D and 3D Imaging

To date, bone mass measured by DXA is the most widely used bone phenotypic measure for genetic population studies of osteoporosis, in large part due to the ubiquitous presence of DXA scanners, which are widely used clinically and in large scale population studies. More recent genetic studies have examined other related phenotypes including hip geometry and shape (270). Looking to the future, DXA scans are likely to contribute to genetic studies of other age-related musculoskeletal conditions. For example, a range of other phenotypes, more closely related to osteoarthritis, are currently being generated in hip and knee DXA scans from approximately 100,000 individuals from UK Biobank<fnref n="4" />. However, a broad number of new technical approaches are under development.

For mammalian and fish studies, an optical coherence tomography (OCT) and synchrotron radiation microcomputed tomography (SR-µCT) may be developed for future use. OCT provides non-invasive high-resolution three-dimensional (3D) images of biological tissue and quantification of chromophores in tissues (271). Fish and human bones contain hydroxyapatite crystals so it can be compared and quantification of BMD is possible to obtain by comparison to sample with known hydroxyapatite levels. Better resolution (100 nm) can be achieved with synchrotron equipped µCT technologies (SR-µCT) with better assessing of bone micro-architecture (11).

Among recently developed MRI techniques is ultrashort echo time (UTE) MRI. Clinical MRI cannot detect water bound to organic matrix, or the free water in the pores of the Haversian system of cortical bone due to the very short apparent transverse relaxation times (T2 *). Therefore, a new class of sequences, ultrashort-TE (UTE) sequences have been developed recently, with TEs of less than 100 µs. This is much shorter than TEs of

conventional sequences. These sequences can be used to detect water signals from within cortical bone (272, 273).

PET scanning can be used for some analyses in bone, and has been used in animal models to assess changes in e.g. bone vascularity or stress fractures (274). However, the reduction of radiation and dose exposures are critical in medical and research imaging, since high doses of radiation are associated with DNA damage. Multiple researchers are actively engaged in the development of clinical total-body PET hardware, promising improvements in dose reductions, reduced scan times, and quantitative kinetic modelling capabilities (275).

Artificial intelligence as new methods for automatic image segmentation, and prediction of fracture risk shows promising clinical value (276). Advances in artificial intelligence (deep learning) also perform well in classifying skeletal radiographs (277). Drug and genetic screening as well as longitudinal studies in zebrafish would benefit from AI, towards implementing novel platforms for gene functional validation through rapid skeletal phenotypic assessment.

## 6.2 Contribution of -omics Technologies to the Phenotypic Dissection of Musculoskeletal Traits

Historically, genome-wide association studies (GWAS) and next generation sequencing have revolutionized genetic diagnostic services and our understanding of common and rare bone diseases. For example, there are now 20+ genes identified that cause osteogenesis imperfecta through whole genome, exome and RNA sequencing and thousands of genetic variants arising from meta-analyses of the Genetic Factors of Osteoporosis (GEFOS) consortium, and the UK Biobank.

Nevertheless, several steps are needed before GWAS discoveries can be translated to biologic processes underlying the genotype-phenotype relationship. First, variants identified by GWAS need to be linked to the gene(s) in the region. Second, such target genes [identified through GWAS or whole exome sequencing (WES)] need to be placed in the context of pathways affected by the genetic variation. Third, functional, mechanistic studies need to establish how the given alteration of the biologic pathway(s) results in a phenotype.

It is here, where the confluence of a roadmap of gene functional evaluations and a detailed assessment of the laboratory and musculoskeletal phenotype can provide mechanistic insight into the relevant biological processes underlying disease. Multi-omics approaches are used to identify laboratory, phenotypic, disease-specific signatures.

Here we provide a succinct overview of multi-omics layers and their importance for bone phenotyping:

**Genomics and Epigenomics:** Gene coding regions (underlying most Mendelian disorders) make up less than 3% of the human genome while, approximately 90% of the single-nucleotide polymorphisms (SNPs) that are associated with human disease lie within intergenic or intronic regions. As such, genetic variation (polymorphisms, insertion/deletions and mutations) in intergenic regions, such as enhancers, can strongly affect gene expression, demonstrating a tight regulatory network between the coding and noncoding parts of the genome.

<footnote n="4">⁴https://www.ukbiobank.ac.uk/.</footnote>
Epigenetic changes at DNA level include methylation, where methyl groups are added to the DNA molecule and change DNA activity, e.g. the repression of gene transcription without change of the DNA sequence. Important examples for these effects include genomic imprinting, X-chromosome inactivation, or repression of transposable elements. Effects on bone have been described in ageing (278) and oncology (279), but also increasingly during metabolic challenges, such as chronic kidney disease (280).

Another important epigenetic modification among others is considered histone tail modifications, where covalent post-translational modification (PTM) of nuclear histone proteins occur *via* methylation, phosphorylation, acetylation, ubiquitination, crotonisation, or sumoylation processes. These changes are currently addressed in a number of bone fields, such as bone and cancer as well as inflammation and rheumatic diseases (281). As an example, for bone/vasculature calcification interactions, a crosstalk between osteogenic transcription factors and histone deacetylases has been described. The inhibition/activation of histone deacetylases might help to develop potential therapeutic interventions in future (282).

For more specific information, see also the publication of GEMSTONE WG3 “Gene & Therapeutic Discoveries in Bone Mass Disorders”.

**Transcriptomics: RNA Sequencing, gene expression, eQTLs, Non-coding RNAs (lncRNA and miRNA)**

Non-coding RNAs (ncRNAs) are recent candidates to become future diagnostic bone biomarkers (283). ncRNA transcripts vary in length from around 22 nucleotides for microRNAs (miRNAs) to more than 200 nucleotides for long non-coding RNAs (lncRNAs). They are found in tissues, but importantly also in body fluids, where they are easier accessible for detection (283, 284).

Different types of ncRNAs are involved in several processes like DNA replication (285, 286), translation (287), RNA splicing (288) and transcriptional regulation (289). Especially miRNAs and lncRNAs are in focus as biomarkers for many conditions, including osteoporosis with some commercially available assays (290–292). They are stable in the bloodstream and protected from RNAse digestion. In the exosomal fraction of body fluids, both miRNAs and lncRNAs are enriched, indicating potentially active secretion of these RNA species from their cells of origin (293, 294).

miRNAs are best studied for their involvement in the control of bone formation and homeostasis through their regulatory functions in osteoblast and osteoclast development (295), not only in metabolic bone diseases, but also in case of cancer and bone metastases. Currently, ncRNA assays are not routinely used and require in most cases a dedicated lab with established PCR procedures. However, ncRNAs may help in the multi-omics phenotype characterization of rare bone diseases and defining disease state in common bone diseases.

**Proteomics and metabolomics: The protein contents, i.e. the proteomes, of tissues and cells logically occupy a central position within the biologic processes underlying genotype-phenotype

relationships. Proteomes can be studied qualitatively and/or quantitatively at a large scale by proteomics (296). Nowadays, mostly based on the use of liquid chromatography and mass spectroscopy methodologies, proteomic approaches can be employed for protein identification and quantification in samples as diverse as tissues, blood and cells, to provide a comprehensive and quantitative information on their proteomes (296). Therefore, a closer inspection of the bone proteome by proteomics is surely a fundamental tool to put in place towards the phenotypic dissection of musculoskeletal traits. For instance, proteomic approaches can help detect changes in the signal transduction of bone cells, in the regulatory mechanisms that govern bone cell differentiation, among other cellular and tissue processes enrolled in bone metabolism in both physiologic and pathological contexts (297). Accordingly up to now, several studies have made important contributions to our knowledge of the bone proteome, as well as, of the proteomes of individual bone cells (reviewed in (297–299). A special emphasis has been given to identify proteomic changes in osteoporosis (300).

Modern metabolomic analysis (analytical chemistry and bioinformatics) is capable of detecting hundreds of metabolites in human serum and hence identify novel biomarkers and biochemical signatures of disease. These topics are further described in the publication of GEMSTONE WG4.

**Microbiomics: Animal studies using germ free mice, antibiotics, probiotics (i.e., microorganisms which confer a health benefit on the host) or prebiotics (i.e., nutrients capable to modify the gut microbiota) have shown that the complex community of microbes colonizing the gastrointestinal tract may regulate bone mass (301, 302).** The impact of major alterations of the gut microbiota has been evaluated using either germ-free mice raised in sterile isolators and completely devoid of microbiota, or rodents depleted of gut microbiota by antibiotic use. These rodent models may be inoculated with specific microbes or communities of microbes to examine the effects they trigger on the skeleton in their host. All these studies using germ free mice or antibiotic treated mice exemplify extreme situations and it might be more physiological to look at the bone effects of treatments resulting in minor but specific changes of an already present gut microbiota. Probiotics and prebiotics given to rodents with an already present gut microbiota have been used to demonstrate that specific changes in the gut microbiota may protect against ovariectomy-induced and inflammation-induced bone loss (303). To characterize the skeletal phenotypes in these rodent bone loss models, standard analyses including DXA of areal BMD, MicroCT of cortical and trabecular bone parameters in the axial and appendicular skeleton, static and dynamic bone histomorphometry, bone strength estimates using three-point bending tests of long bones, *in vitro* studies of primary cultures of osteoblasts and osteoclasts precursors as well as analyses of circulating BTMs can be used (304).

The first promising findings of two randomized clinical probiotic treatment trials recently revealed that certain probiotic
Epigenetic changes at DNA level include methylation, where methyl groups are added to the DNA molecule and change DNA activity, e.g. the repression of gene transcription without change of the DNA sequence. Important examples for these effects include genomic imprinting, X-chromosome inactivation, or repression of transposable elements. Effects on bone have been described in ageing (278) and oncology (279), but also increasingly during metabolic challenges, such as chronic kidney disease (280).

Another important epigenetic modification among others is considered histone tail modifications, where covalent post-translational modification (PTM) of nuclear histone proteins occur *via* methylation, phosphorylation, acetylation, ubiquitination, crotonisation, or sumoylation processes. These changes are currently addressed in a number of bone fields, such as bone and cancer as well as inflammation and rheumatic diseases (281). As an example, for bone/vasculature calcification interactions, a crosstalk between osteogenic transcription factors and histone deacetylases has been described. The inhibition/activation of histone deacetylases might help to develop potential therapeutic interventions in future (282).

For more specific information, see also the publication of GEMSTONE WG3 “Gene & Therapeutic Discoveries in Bone Mass Disorders”.

**Transcriptomics: RNA Sequencing, gene expression, eQTLs, Non-coding RNAs (lncRNA and miRNA)**

Non-coding RNAs (ncRNAs) are recent candidates to become future diagnostic bone biomarkers (283). ncRNA transcripts vary in length from around 22 nucleotides for microRNAs (miRNAs) to more than 200 nucleotides for long non-coding RNAs (lncRNAs). They are found in tissues, but importantly also in body fluids, where they are easier accessible for detection (283, 284).

Different types of ncRNAs are involved in several processes like DNA replication (285, 286), translation (287), RNA splicing (288) and transcriptional regulation (289). Especially miRNAs and lncRNAs are in focus as biomarkers for many conditions, including osteoporosis with some commercially available assays (290–292). They are stable in the bloodstream and protected from RNAse digestion. In the exosomal fraction of body fluids, both miRNAs and lncRNAs are enriched, indicating potentially active secretion of these RNA species from their cells of origin (293, 294).

miRNAs are best studied for their involvement in the control of bone formation and homeostasis through their regulatory functions in osteoblast and osteoclast development (295), not only in metabolic bone diseases, but also in case of cancer and bone metastases. Currently, ncRNA assays are not routinely used and require in most cases a dedicated lab with established PCR procedures. However, ncRNAs may help in the multi-omics phenotype characterization of rare bone diseases and defining disease state in common bone diseases.

**Proteomics and metabolomics: The protein contents, i.e. the proteomes, of tissues and cells logically occupy a central position within the biologic processes underlying genotype-phenotype

relationships. Proteomes can be studied qualitatively and/or quantitatively at a large scale by proteomics (296). Nowadays, mostly based on the use of liquid chromatography and mass spectroscopy methodologies, proteomic approaches can be employed for protein identification and quantification in samples as diverse as tissues, blood and cells, to provide a comprehensive and quantitative information on their proteomes (296). Therefore, a closer inspection of the bone proteome by proteomics is surely a fundamental tool to put in place towards the phenotypic dissection of musculoskeletal traits. For instance, proteomic approaches can help detect changes in the signal transduction of bone cells, in the regulatory mechanisms that govern bone cell differentiation, among other cellular and tissue processes enrolled in bone metabolism in both physiologic and pathological contexts (297). Accordingly up to now, several studies have made important contributions to our knowledge of the bone proteome, as well as, of the proteomes of individual bone cells (reviewed in (297–299). A special emphasis has been given to identify proteomic changes in osteoporosis (300).

Modern metabolomic analysis (analytical chemistry and bioinformatics) is capable of detecting hundreds of metabolites in human serum and hence identify novel biomarkers and biochemical signatures of disease. These topics are further described in the publication of GEMSTONE WG4.

**Microbiomics: Animal studies using germ free mice, antibiotics, probiotics (i.e., microorganisms which confer a health benefit on the host) or prebiotics (i.e., nutrients capable to modify the gut microbiota) have shown that the complex community of microbes colonizing the gastrointestinal tract may regulate bone mass (301, 302).** The impact of major alterations of the gut microbiota has been evaluated using either germ-free mice raised in sterile isolators and completely devoid of microbiota, or rodents depleted of gut microbiota by antibiotic use. These rodent models may be inoculated with specific microbes or communities of microbes to examine the effects they trigger on the skeleton in their host. All these studies using germ free mice or antibiotic treated mice exemplify extreme situations and it might be more physiological to look at the bone effects of treatments resulting in minor but specific changes of an already present gut microbiota. Probiotics and prebiotics given to rodents with an already present gut microbiota have been used to demonstrate that specific changes in the gut microbiota may protect against ovariectomy-induced and inflammation-induced bone loss (303). To characterize the skeletal phenotypes in these rodent bone loss models, standard analyses including DXA of areal BMD, MicroCT of cortical and trabecular bone parameters in the axial and appendicular skeleton, static and dynamic bone histomorphometry, bone strength estimates using three-point bending tests of long bones, *in vitro* studies of primary cultures of osteoblasts and osteoclasts precursors as well as analyses of circulating BTMs can be used (304).

The first promising findings of two randomized clinical probiotic treatment trials recently revealed that certain probiotic
treatments had some bone sparing effects on DXA measurements of areal BMD at the lumbar spine (305) or by CT measurements of volumetric BMD of the distal tibia in humans (306).

The results from the first human cross-sectional association studies between the gut microbiota composition and bone related parameters have yielded conflicting results, most likely as some of the studies were underpowered and not adjusted for major confounders affecting the gut microbiota composition. Large-scale population-based studies assessing the association between the gut microbiome composition, as assessed by cost efficient 16S rRNA sequencing, and DXA-derived phenotypes adjusting for relevant confounders including lifestyle factors, diet and medications are underway. Furthermore, meta-genome wide association studies using state of the art sequence methodology in combination with other omics platforms should be performed to characterize functional gut microbiota signatures associated with human bone health in detail. In addition, these analyses should not be restricted to DXA-derived areal BMD but include bone architecture and dimensions, specific cortical and trabecular bone parameters and incident fracture risk.

## CONCLUSION AND THE TRIANGULATION OF [DIAGNOSTIC] EVIDENCE: THE PATH TO PERSONALIZED MEDICINE

Understanding complex systems such as the skeleton requires the integration of multiple layers of evidence arising from a combination of analytical methods, in humans and animals, as outlined in this publication. Integration across many disciplines is required to solve outstanding questions and create a “deep phenotype” which accurately captures disease signatures. Key to this is translatability – whether across methodologies or species – and synchronization of efforts. The ‘triangulation of evidence’ (307) can overcome logistical and ethical constraints related to experimental design and speed the rate at which the reality of personalized medicine is attained. Looking to the future, there are several areas where the utility of animal models is obvious. For example, skeletal effects associated with transgender pharmacotherapy have not yet been widely studied. Issues include start-time for treatment, particularly with regard to puberty, and the short- and long-term effects from cross-sex therapies on mineral metabolism. Further, on a broader scale ‘duration of exposure’ to a given risk factor during the life course is of interest for skeletal health from an epigenetic perspective. In the young, damaging exposures have not yet accumulated, while with age, these contribute to accelerated biological aging. In conclusion, the idea of triangulation of evidence emerges as a solid way to weigh the

robustness of each layer of evidence, but most importantly to gain insight from the integrated systems perspective. Similarly, the triangulation approach helps to overcome severe logistical and ethical constraints on experimental design potentially arising at each level. Once etiologic validity has been satisfactorily established across the different dimensions, the next challenge is to amalgamate these into a “deep phenotype”. This assembled deep phenotype will pave the road to find drug targets and clinical applications, ultimately charting the course toward personalized medicine approaches, for each of us.

## AUTHOR CONTRIBUTIONS

IF and BO-P initiated and organised the manuscript. IF, FM, WH, and BO-P generated the figures for the manuscript. All authors contributed to the article and approved the submitted version.

## FUNDING

Funding was obtained from the GEMSTONE COST Action (CA18139). The Origins of Bone and Cartilage Disease Programme analysed the skeletal phenotypes of knockout mice generated by the International Mouse Phenotyping Consortium (IMPC) and was funded by a Welcome Trust Strategic Award (101123) to GRW and JHDB. IF is enrolled in the PhD program MOLIN at the Medical University of Graz, funded by the Austrian Science Fund (FWF). FM is funded by the Swedish Research Council (2018-02981), Greta and Johan Kock Foundation, the A. Påhlsson, A. Osterlund Foundation and H Järnhardt Foundations, King Gustav V 80 year fund, Swedish Rheumatism foundation, Skåne University Hospital Research Fund, Research and Development Council of Region Skåne.

## ACKNOWLEDGMENTS

This publication is based upon work from COST Action GEMSTONE (CA18139), supported by COST (European Cooperation in Science and Technology). COST is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. (www cost.eu). The Graphical Abstract and **Figure 3** are created with BioRender (www.BioRender.com).

## REFERENCES

1. Sobacchi C, Menale C, Villa A. The RANKL-RANK Axis: A Bone to Thymus Round Trip. *Front Immunol* (2019) 10. doi: 10.3389/fimmu.2019.00629

2. Zaiss MM, Jones RM, Schett G, Pacifici R. The Gut-Bone Axis: How Bacterial Metabolites Bridge the Distance. *J Clin Invest* (2019) 129:3018-28. doi: 10.1172/JCI128521

3. Behera J, Ison J, Tyagi SC, Tyagi N. The Role of Gut Microbiota in Bone Homeostasis. *Bone* (2020) 135. doi: 10.1016/j.bone.2020.115317

4. Lems WF, Raterman HG. Critical Issues and Current Challenges in Osteoporosis and Fracture Prevention. An Overview of Unmet Needs. *Ther Adv Musculoskelet Dis* (2017) 9(12):299-316. doi: 10.1177/1759720X17732562

5. Kan L. Animal Models of Bone Diseases-A. In: *Animal Models for the Study of Human Disease*. Elsevier Inc (2013). p. 353-90.
treatments had some bone sparing effects on DXA measurements of areal BMD at the lumbar spine (305) or by CT measurements of volumetric BMD of the distal tibia in humans (306).

The results from the first human cross-sectional association studies between the gut microbiota composition and bone related parameters have yielded conflicting results, most likely as some of the studies were underpowered and not adjusted for major confounders affecting the gut microbiota composition. Large-scale population-based studies assessing the association between the gut microbiome composition, as assessed by cost efficient 16S rRNA sequencing, and DXA-derived phenotypes adjusting for relevant confounders including lifestyle factors, diet and medications are underway. Furthermore, meta-genome wide association studies using state of the art sequence methodology in combination with other omics platforms should be performed to characterize functional gut microbiota signatures associated with human bone health in detail. In addition, these analyses should not be restricted to DXA-derived areal BMD but include bone architecture and dimensions, specific cortical and trabecular bone parameters and incident fracture risk.

## CONCLUSION AND THE TRIANGULATION OF [DIAGNOSTIC] EVIDENCE: THE PATH TO PERSONALIZED MEDICINE

Understanding complex systems such as the skeleton requires the integration of multiple layers of evidence arising from a combination of analytical methods, in humans and animals, as outlined in this publication. Integration across many disciplines is required to solve outstanding questions and create a “deep phenotype” which accurately captures disease signatures. Key to this is translatability – whether across methodologies or species – and synchronization of efforts. The ‘triangulation of evidence’ (307) can overcome logistical and ethical constraints related to experimental design and speed the rate at which the reality of personalized medicine is attained. Looking to the future, there are several areas where the utility of animal models is obvious. For example, skeletal effects associated with transgender pharmacotherapy have not yet been widely studied. Issues include start-time for treatment, particularly with regard to puberty, and the short- and long-term effects from cross-sex therapies on mineral metabolism. Further, on a broader scale ‘duration of exposure’ to a given risk factor during the life course is of interest for skeletal health from an epigenetic perspective. In the young, damaging exposures have not yet accumulated, while with age, these contribute to accelerated biological aging. In conclusion, the idea of triangulation of evidence emerges as a solid way to weigh the

robustness of each layer of evidence, but most importantly to gain insight from the integrated systems perspective. Similarly, the triangulation approach helps to overcome severe logistical and ethical constraints on experimental design potentially arising at each level. Once etiologic validity has been satisfactorily established across the different dimensions, the next challenge is to amalgamate these into a “deep phenotype”. This assembled deep phenotype will pave the road to find drug targets and clinical applications, ultimately charting the course toward personalized medicine approaches, for each of us.

## AUTHOR CONTRIBUTIONS

IF and BO-P initiated and organised the manuscript. IF, FM, WH, and BO-P generated the figures for the manuscript. All authors contributed to the article and approved the submitted version.

## FUNDING

Funding was obtained from the GEMSTONE COST Action (CA18139). The Origins of Bone and Cartilage Disease Programme analysed the skeletal phenotypes of knockout mice generated by the International Mouse Phenotyping Consortium (IMPC) and was funded by a Welcome Trust Strategic Award (101123) to GRW and JHDB. IF is enrolled in the PhD program MOLIN at the Medical University of Graz, funded by the Austrian Science Fund (FWF). FM is funded by the Swedish Research Council (2018-02981), Greta and Johan Kock Foundation, the A. Påhlsson, A. Osterlund Foundation and H Järnhardt Foundations, King Gustav V 80 year fund, Swedish Rheumatism foundation, Skåne University Hospital Research Fund, Research and Development Council of Region Skåne.

## ACKNOWLEDGMENTS

This publication is based upon work from COST Action GEMSTONE (CA18139), supported by COST (European Cooperation in Science and Technology). COST is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. (www cost.eu). The Graphical Abstract and **Figure 3** are created with BioRender (www.BioRender.com).

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88. Pediatric Positions - ISCD. Available at: https://iscd.org/learn/official- positions/pediatric-positions/.

89. Newham E, Hague E, Aggleton JA, Fernee C, Brown KR, Hammond CL. Finite Element and Deformation Analyses Predict Pattern of Bone Failure in Loaded Zebrafish Spines. *J R Soc Interface* (2019) 16(160):20190430. doi: 10.1098/rsif.2019.0430

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91. Lerchbaum E, Trummer C, Theiler-Schwetz V, Kollmann M, Wölfler M, Pilz S, et al. Effects of Vitamin D Supplementation on Bone Turnover and Bone Mineral Density in Healthy Men: A Post-Hoc Analysis of a Randomized Controlled Trial. *Nutrients* (2019) 11(4):731. doi: 10.3390/nu11040731

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93. Leslie WD, Aubry-Rozier B, Lamy O, Hans D. TBS (Trabecular Bone Score) and Diabetes-Related Fracture Risk. *J Clin Endocrinol Metab* (2013) 98 (2):602-9. doi: 10.1210/jc.2012-3118

94. Richards C, Hans D, Leslie WD. Trabecular Bone Score (TBS) Predicts Fracture in Ankylosing Spondylitis: The Manitoba BMD Registry. *J Clin Densitom* (2020) 23(4):543-8. doi: 10.1016/j.jcdd.2020.01.003

95. Shevroja E, Aubry-Rozier B, Hans G, Gonzalez-Rodriguez E, Stoll D, Lamy O, et al. Clinical Performance of the Updated Trabecular Bone Score (TBS) Algorithm, Which Accounts for the Soft Tissue Thickness: The OsteoLaus Study. *J Bone Miner Res* (2019) 34(12):2229-37. doi: 10.1002/jbmr.3851

96. Guagnelli MA, Winzenried R, Lopez-Gonzalez D, McClung MR, Del Rio L, Clark P. Bone Age as a Correction Factor for the Analysis of Trabecular Bone Score (TBS) in Children. *Arch Osteoporos* (2019) 14(1):1-7. doi: 10.1007/ s11657-019-0573-6

97. Jazinizadeh F, Quenneville CE. Enhancing Hip Fracture Risk Prediction by Statistical Modeling and Texture Analysis on DXA Images. *Med Eng Phys* (2020) 78:14-20. doi: 10.1016/j.medengphy.2020.01.015

98. vom Scheidt A, Grisolia Seifert EF, Pokrant C, Püschel K, Amling M, Busse B, et al. Subregional Areal Bone Mineral Density (aBMD) is a Better Predictor of Heterogeneity in Trabecular Microstructure of Vertebrae in Young and Aged Women Than Subregional Trabecular Bone Score (TBS). *Bone* (2019) 122:156-65. doi: 10.1016/j.bone.2019.02.014

99. Glüer CC. Quantitative Ultrasound Techniques for the Assessment of Osteoporosis: Expert Agreement on Current Status. *J Bone Miner Res* (1997) 12(8):1280-8. doi: 10.1359/jbmr.1997.12.8.1280

100. Hans D, Dargent-Molina P, Schott AM, Sebert JL, Cormier C, Kotzki PO, et al. Ultrasoundographic Heel Measurements to Predict Hip Fracture in Elderly Women: The EPIDOS Prospective Study. *Lancet* (1996) 348(9026):511-4. doi: 10.1016/S0140-6736(95)11456-4

101. Knapp KM. Quantitative Ultrasound and Bone Health. *Salud Publica Mex* (2009) 51(SUPPL.1):S18-24. doi: 10.1590/S0036-36342009000700005

102. Kemp JP, Morris JA, Medina-Gomez C, Forgetta V, Warrington NM, Youlten SE, et al. Identification of 153 New Loci Associated With Heel Bone Mineral Density and Functional Involvement of GPC6 in Osteoporosis. *Nat Genet* (2017) 49(10):1468-75. doi: 10.1038/ng.3949

103. Ackermann O, Simanowski J, Eckert K. Fracture Ultrasound of the Extremities. *Ultraschall der Medizin* (2020) 41(1):12-28. doi: 10.1055/a-1023-1782

104. Kanis JA, Melton LJ, Christiansen C, Johnston CC, Khaltaev N. The Diagnosis of Osteoporosis. *J Bone Miner Res* (1994) 9(8):1137-41. doi: 10.1002/jbmr.5650090802

105. Lentle BC, Hammond I, Firth GB, Sutton RAL. Imaging of Osteoporotic Fractures on XR, CT, and MR. *Curr Radiol Rep* (2014) 2:1-9. doi: 10.1007/ s40134-013-0032-x

106. O'Ryan FS, Khoury S, Liao W, Han MM, Hui RL, Baer D, et al. Intravenous Bisphosphonate-Related Osteonecrosis of the Jaw: Bone Scintigraphy as an Early Indicator. *J Oral Maxillofac Surg* (2009) 67(7):1363-72. doi: 10.1016/ j.joms.2009.03.005

107. Grüneboom A, Kling L, Christiansen S, Mill L, Maier A, Engelke K, et al. Next-Generation Imaging of the Skeletal System and its Blood Supply. *Nat Rev Rheumatol* (2019) 15:533-49. doi: 10.1038/s41584-019-0274-y

108. Huang K, Feng Y, Liu D, Liang W, Li L. Quantification Evaluation of 99mtc-MDP Concentration in the Lumbar Spine With SPECT/CT: Compare With Bone Mineral Density. *Ann Nucl Med* (2020) 34(2):136-43. doi: 10.1007/ s12149-019-01425-x

109. Li YB, Zheng X, Wang R, Wu H, Han S, Deng ZY, et al. SPECT-CT Versus MRI in Localizing Active Lesions in Patients With Osteoporotic Vertebral Compression Fractures. *Nucl Med Commun* (2018) 39(7):610-7. doi: 10.1097/MNM.0000000000000857

110. Butterfield NC, Logan JG, Waung J, Williams GR, Bassett JHD. Quantitative X-Ray Imaging of Mouse Bone by Faxitron. In: *Methods in Molecular Biology*. New York, NY: Humana Press (2019). p. 559-69.

111. Khalil MM, Tremoleda JL, Bayomy TB, Gsell W. Molecular SPECT Imaging: An Overview. *Int J Mol Imaging* (2011) 2011:1-15. doi: 10.1155/2011/796025

112. Tremoleda JL, Khalil M, Gompels LL, Wylezinska-Arridge M, Vincent T, Gsell W. Imaging Technologies for Preclinical Models of Bone and Joint Disorders. *EJNMMI Res* (2011) 1(1):1-14. doi: 10.1186/2191-219X-1-11

113. Lienemann PS, Metzger S, Kiveliö AS, Blanc A, Papageorgiou P, Astolfo A, et al. Longitudinal *In Vivo* Evaluation of Bone Regeneration by Combined Measurement of Multi-Pinhole SPECT and Micro-CT for Tissue Engineering. *Sci Rep* (2015) 5:10238. doi: 10.1038/srep10238

114. Adams JE. Quantitative Computed Tomography. *Eur J Radiol* (2009) 71 (3):415-24. doi: 10.1016/j.ejrad.2009.04.074

115. Lang TF. Quantitative Computed Tomography. *Radiologic Clinics North America* (2010) 48:589-600. doi: 10.1016/j.rcl.2010.03.001

116. Engelke K, van Rietbergen B, Zysset P. FEA to Measure Bone Strength: A Review. *Clin Rev Bone Mineral Metab* (2016) 14:26-37. doi: 10.1007/s12018- 015-9201-1

117. Johannesdottir F, Allaire B, Bouxsein ML. Fracture Prediction by Computed Tomography and Finite Element Analysis: Current and Future Perspectives. *Curr Osteoporosis Rep* (2018) 16(4):411-22. doi: 10.1007/s11914-018-0450-z

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84. Chou SH, LeBoff MS. Vertebral Imaging in the Diagnosis of Osteoporosis: A Clinician's Perspective. *Curr Osteoporosis Rep* (2017) 15:509-20. doi: 10.1007/s11914-017-0404-x

85. Adult Positions - ISCD. Available at: https://iscd.org/learn/official-positions/ adult-positions/.

86. Lewiecki EM, Gordon CM, Baim S, Binkley N, Bilezikian JP, Kendler DL, et al. Special Report on the 2007 Adult and Pediatric Position Development Conferences of the International Society for Clinical Densitometry. *Osteoporos Int* (2008) 19(10):1369-78. doi: 10.1007/s00198-008-0689-9

87. Cosman F, de Beur SJ, LeBoff MS, Lewiecki EM, Tanner B, Randall S, et al. Clinician's Guide to Prevention and Treatment of Osteoporosis. *Osteoporos Int* (2014) 25(10):2359-81. doi: 10.1007/s00198-014-2794-2

88. Pediatric Positions - ISCD. Available at: https://iscd.org/learn/official-positions/pediatric-positions/.

89. Newham E, Hague E, Aggleton JA, Fernee C, Brown KR, Hammond CL. Finite Element and Deformation Analyses Predict Pattern of Bone Failure in Loaded Zebrafish Spines. *J R Soc Interface* (2019) 16(160):20190430. doi: 10.1098/rsif.2019.0430

90. Sousa S, Valerio F, Jacinto A. A New Zebrafish Bone Crush Injury Model. *Biol Open* (2012) 1(9):915-21. doi: 10.1242/bio.2012877

91. Lerchbaum E, Trummer C, Theiler-Schwetz V, Kollmann M, Wölfler M, Pilz S, et al. Effects of Vitamin D Supplementation on Bone Turnover and Bone Mineral Density in Healthy Men: A Post-Hoc Analysis of a Randomized Controlled Trial. *Nutrients* (2019) 11(4):731. doi: 10.3390/nu11040731

92. Mirzaali MJ, Libonati F, Ferrario D, Rinaudo L, Messina C, Ulivieri FM, et al. Determinants of Bone Damage: An Ex-Vivo Study on Porcine Vertebrae. Vashishth D, Editor. *PloS One* (2018) 13(8):e0202210. doi: 10.1371/journal.pone.0202210

93. Leslie WD, Aubry-Rozier B, Lamy O, Hans D. TBS (Trabecular Bone Score) and Diabetes-Related Fracture Risk. *J Clin Endocrinol Metab* (2013) 98 (2):602-9. doi: 10.1210/jc.2012-3118

94. Richards C, Hans D, Leslie WD. Trabecular Bone Score (TBS) Predicts Fracture in Ankylosing Spondylitis: The Manitoba BMD Registry. *J Clin Densitom* (2020) 23(4):543-8. doi: 10.1016/j.jcdd.2020.01.003

95. Shevroja E, Aubry-Rozier B, Hans G, Gonzalez-Rodriguez E, Stoll D, Lamy O, et al. Clinical Performance of the Updated Trabecular Bone Score (TBS) Algorithm, Which Accounts for the Soft Tissue Thickness: The OsteoLaus Study. *J Bone Miner Res* (2019) 34(12):2229-37. doi: 10.1002/jbmr.3851

96. Guagnelli MA, Winzenried R, Lopez-Gonzalez D, McClung MR, Del Rio L, Clark P. Bone Age as a Correction Factor for the Analysis of Trabecular Bone Score (TBS) in Children. *Arch Osteoporos* (2019) 14(1):1-7. doi: 10.1007/s11657-019-0573-6

97. Jazinizadeh F, Quenneville CE. Enhancing Hip Fracture Risk Prediction by Statistical Modeling and Texture Analysis on DXA Images. *Med Eng Phys* (2020) 78:14-20. doi: 10.1016/j.medengphy.2020.01.015

98. vom Scheidt A, Grisolia Seifert EF, Pokrant C, Püschel K, Amling M, Busse B, et al. Subregional Areal Bone Mineral Density (aBMD) is a Better Predictor of Heterogeneity in Trabecular Microstructure of Vertebrae in Young and Aged Women Than Subregional Trabecular Bone Score (TBS). *Bone* (2019) 122:156-65. doi: 10.1016/j.bone.2019.02.014

99. Glüer CC. Quantitative Ultrasound Techniques for the Assessment of Osteoporosis: Expert Agreement on Current Status. *J Bone Miner Res* (1997) 12(8):1280-8. doi: 10.1359/jbmr.1997.12.8.1280

100. Hans D, Dargent-Molina P, Schott AM, Sebert JL, Cormier C, Kotzki PO, et al. Ultrasoundographic Heel Measurements to Predict Hip Fracture in Elderly Women: The EPIDOS Prospective Study. *Lancet* (1996) 348(9026):511-4. doi: 10.1016/S0140-6736(95)11456-4

101. Knapp KM. Quantitative Ultrasound and Bone Health. *Salud Publica Mex* (2009) 51(SUPPL.1):S18-24. doi: 10.1590/S0036-36342009000700005

102. Kemp JP, Morris JA, Medina-Gomez C, Forgetta V, Warrington NM, Youlten SE, et al. Identification of 153 New Loci Associated With Heel Bone Mineral Density and Functional Involvement of GPC6 in Osteoporosis. *Nat Genet* (2017) 49(10):1468-75. doi: 10.1038/ng.3949

103. Ackermann O, Simanowski J, Eckert K. Fracture Ultrasound of the Extremities. *Ultraschall der Medizin* (2020) 41(1):12-28. doi: 10.1055/a-1023-1782

104. Kanis JA, Melton LJ, Christiansen C, Johnston CC, Khaltaev N. The Diagnosis of Osteoporosis. *J Bone Miner Res* (1994) 9(8):1137-41. doi: 10.1002/jbmr.5650090802

105. Lentle BC, Hammond I, Firth GB, Sutton RAL. Imaging of Osteoporotic Fractures on XR, CT, and MR. *Curr Radiol Rep* (2014) 2:1-9. doi: 10.1007/s40134-013-0032-x

106. O'Ryan FS, Khoury S, Liao W, Han MM, Hui RL, Baer D, et al. Intravenous Bisphosphonate-Related Osteonecrosis of the Jaw: Bone Scintigraphy as an Early Indicator. *J Oral Maxillofac Surg* (2009) 67(7):1363-72. doi: 10.1016/j.joms.2009.03.005

107. Grüneboom A, Kling L, Christiansen S, Mill L, Maier A, Engelke K, et al. Next-Generation Imaging of the Skeletal System and its Blood Supply. *Nat Rev Rheumatol* (2019) 15:533-49. doi: 10.1038/s41584-019-0274-y

108. Huang K, Feng Y, Liu D, Liang W, Li L. Quantification Evaluation of 99mtc-MDP Concentration in the Lumbar Spine With SPECT/CT: Compare With Bone Mineral Density. *Ann Nucl Med* (2020) 34(2):136-43. doi: 10.1007/s12149-019-01425-x

109. Li YB, Zheng X, Wang R, Wu H, Han S, Deng ZY, et al. SPECT-CT Versus MRI in Localizing Active Lesions in Patients With Osteoporotic Vertebral Compression Fractures. *Nucl Med Commun* (2018) 39(7):610-7. doi: 10.1097/MNM.0000000000000857

110. Butterfield NC, Logan JG, Waung J, Williams GR, Bassett JHD. Quantitative X-Ray Imaging of Mouse Bone by Faxitron. In: *Methods in Molecular Biology*. New York, NY: Humana Press (2019). p. 559-69.

111. Khalil MM, Tremoleda JL, Bayomy TB, Gsell W. Molecular SPECT Imaging: An Overview. *Int J Mol Imaging* (2011) 2011:1-15. doi: 10.1155/2011/796025

112. Tremoleda JL, Khalil M, Gompels LL, Wylezinska-Arridge M, Vincent T, Gsell W. Imaging Technologies for Preclinical Models of Bone and Joint Disorders. *EJNMMI Res* (2011) 1(1):1-14. doi: 10.1186/2191-219X-1-11

113. Lienemann PS, Metzger S, Kiveliö AS, Blanc A, Papageorgiou P, Astolfo A, et al. Longitudinal *In Vivo* Evaluation of Bone Regeneration by Combined Measurement of Multi-Pinhole SPECT and Micro-CT for Tissue Engineering. *Sci Rep* (2015) 5:10238. doi: 10.1038/srep10238

114. Adams JE. Quantitative Computed Tomography. *Eur J Radiol* (2009) 71 (3):415-24. doi: 10.1016/j.ejrad.2009.04.074

115. Lang TF. Quantitative Computed Tomography. *Radiologic Clinics North America* (2010) 48:589-600. doi: 10.1016/j.rcl.2010.03.001

116. Engelke K, van Rietbergen B, Zysset P. FEA to Measure Bone Strength: A Review. *Clin Rev Bone Mineral Metab* (2016) 14:26-37. doi: 10.1007/s12018-015-9201-1

117. Johannesdottir F, Allaire B, Bouxsein ML. Fracture Prediction by Computed Tomography and Finite Element Analysis: Current and Future Perspectives. *Curr Osteoporosis Rep* (2018) 16(4):411-22. doi: 10.1007/s11914-018-0450-z

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124. Stagi S, Cavalli L, Cavalli T, De Martino M, Brandi ML. Peripheral Quantitative Computed Tomography (pQCT) for the Assessment of Bone Strength in Most of Bone Affecting Conditions in Developmental Age: A Review. *Ital J Pediatrics* (2016) 42:1-20. doi: 10.1186/s13052-016-0297-9

125. Paternoster L, Lorentzon M, Vandenput L, Karlsson MK, Ljunggren Ö, Kindmark A, et al. Genome-Wide Association Meta-Analysis of Cortical Bone Mineral Density Unravels Allelic Heterogeneity at the RANKL Locus
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126. Paternoster L, Lorentzon M, Lehtimäki T, Eriksson J, Kähönen M, Raitakari O, et al. Genetic Determinants of Trabecular and Cortical Volumetric Bone Mineral Densities and Bone Microstructure. *Richards JB, Editor. PloS Genet* (2013) 9(2):e1003247. doi: 10.1371/journal.pgen.1003247

127. Zheng H-F, Tobias JH, Duncan E, Evans DM, Eriksson J, Paternoster L, et al. WNT16 Influences Bone Mineral Density, Cortical Bone Thickness, Bone Strength, and Osteoporotic Fracture Risk. *Gibson G, Editor. PloS Genet* (2012) 8(7):e1002745. doi: 10.1371/journal.pgen.1002745

128. Compston J. Type 2 Diabetes Mellitus and Bone. *J Intern Med* (2018) 283 (2):140-53. doi: 10.1111/joim.12725

129. Gasser JA, Willnecker J. Bone Measurements by Peripheral Quantitative Computed Tomography in Rodents. *Methods Mol Biol* (2019) 1914:533-58. doi: 10.1007/978-1-4939-8997-3_29

130. Schmidt C, Priemel M, Kohler T, Weusten A, Müller R, Amling M, et al. Precision and Accuracy of Peripheral Quantitative Computed Tomography (pQCT) in the Mouse Skeleton Compared With Histology and Microcomputed Tomography (µct). *J Bone Miner Res* (2003) 18(8):1486-96. doi: 10.1359/jbmr.2003.18.8.1486

131. Brodt MD, Pelz GB, Taniguchi J, Silva MJ. Accuracy of Peripheral Quantitative Computed Tomography (pQCT) for Assessing Area and Density of Mouse Cortical Bone. *Calcif Tissue Int* (2003) 73(4):411-8. doi: 10.1007/s00223-002-0006-0

132. Manske SL, Zhu Y, Sandino C, Boyd SK. Human Trabecular Bone Microarchitecture can be Assessed Independently of Density With Second Generation HR-pQCT. *Bone* (2015) 79:213-21. doi: 10.1016/j.bone.2015.06.006

133. Whittier DE, Boyd SK, Burghardt AJ, Paccou J, Ghasem-Zadeh A, Chapurlat R, et al. Guidelines for the Assessment of Bone Density and Microarchitecture *In Vivo* Using High-Resolution Peripheral Quantitative Computed Tomography. *Osteoporos Int* (2020) 31(9):1607-27. doi: 10.1007/s00198-020-05438-5

134. Boutroy S, Bouxsein ML, Munoz F, Delmas PD. *In Vivo* Assessment of Trabecular Bone Microarchitecture by High-Resolution Peripheral Quantitative Computed Tomography. *J Clin Endocrinol Metab* (2005) 90 (12):6508-15. doi: 10.1210/jc.2005-1258

135. Laib A, Hauselmann HJ, Ruegsegger P. *In Vivo* High Resolution 3D-QCT of the Human Forearm. *Technol Heal Care* (1998) 6(5-6):329-37. doi: 10.3233/THC-1998-65-606

136. Shanbhogue VV, Brixen K, Hansen S. Age- and Sex-Related Changes in Bone Microarchitecture and Estimated Strength: A Three-Year Prospective Study Using HRpQCT. *J Bone Miner Res* (2016) 31(8):1541-9. doi: 10.1002/jbmr.2817

137. Hamilton EJ, Ghasem-Zadeh A, Gianatti E, Lim-Joon D, Bolton D, Zebaze R, et al. Structural Decay of Bone Microarchitecture in Men With Prostate Cancer Treated With Androgen Deprivation Therapy. *J Clin Endocrinol Metab* (2010) 95(12):E456-63. doi: 10.1210/jc.2010-0902

138. van Rietbergen B, Ito K. A Survey of Micro-Finite Element Analysis for Clinical Assessment of Bone Strength: The First Decade. *J Biomech* (2015) 48 (5):832-41. doi: 10.1016/j.jbiomech.2014.12.024

139. Whittier DE, Manske SL, Kiel DP, Bouxsein M, Boyd SK. Harmonizing Finite Element Modelling for non-Invasive Strength Estimation by High-Resolution Peripheral Quantitative Computed Tomography. *J Biomech* (2018) 80:63-71. doi: 10.1016/j.jbiomech.2018.08.030

140. Kroker A, Zhu Y, Manske SL, Barber R, Mohtadi N, Boyd SK. Quantitative *In Vivo* Assessment of Bone Microarchitecture in the Human Knee Using HR-pQCT. *Bone* (2017) 97:43-8. doi: 10.1016/j.bone.2016.12.015

141. Sada K, Chiba K, Kajiyama S, Okazaki N, Yonekura A, Tomita M, et al. Bone Mineral Density and Microstructure of the Elbow in Baseball Pitchers: An Analysis by Second-Generation HR-pQCT. *J Clin Densitom* (2020) 23 (2):322-8. doi: 10.1016/j.jocd.2019.03.001

142. Zebaze R, Seeman E. Cortical Bone: A Challenging Geography. *J Bone Miner Res* (2015) 30(1):24-9. doi: 10.1002/jbmr.2419

143. Nishiyama KK, Macdonald HM, Buie HR, Hanley DA, Boyd SK. Postmenopausal Women With Osteopenia Have Higher Cortical Porosity and Thinner Cortices at the Distal Radius and Tibia Than Women With

Normal aBMD: An *In Vivo* HR-pQCT Study. *J Bone Miner Res* (2009) 25 (4):901019190442039-30. doi: 10.1359/jbmr.091020

144. Burghardt AJ, Buie HR, Laib A, Majumdar S, Boyd SK. Reproducibility of Direct Quantitative Measures of Cortical Bone Microarchitecture of the Distal Radius and Tibia by HR-pQCT. *Bone* (2010) 47(3):519-28. doi: 10.1016/j.bone.2010.05.034

145. Zebaze R, Ghasem-Zadeh A, Mbala A, Seeman E. A New Method of Segmentation of Compact-ApPEARing, Transitional and Trabecular Compartments and Quantification of Cortical Porosity From High Resolution Peripheral Quantitative Computed Tomographic Images. *Bone* (2013) 54(1):8-20. doi: 10.1016/j.bone.2013.01.007

146. Burghardt AJ, Pialat JB, Kazakia GJ, Boutroy S, Engelke K, Patsch JM, et al. Multicenter Precision of Cortical and Trabecular Bone Quality Measures Assessed by High-Resolution Peripheral Quantitative Computed Tomography. *J Bone Miner Res* (2013) 28(3):524-36. doi: 10.1002/jbmr.1795

147. Piot A, Chapurlat RD, Claustrat B, Szulc P. Relationship Between Sex Steroids and Deterioration of Bone Microarchitecture in Older Men: The Prospective STRAMBO Study. *J Bone Miner Res* (2019) 34(9):1562-73. doi: 10.1002/jbmr.3746

148. Samelson EJ, Broe KE, Xu H, Yang L, Boyd S, Biver E, et al. Cortical and Trabecular Bone Microarchitecture as an Independent Predictor of Incident Fracture Risk in Older Women and Men in the Bone Microarchitecture International Consortium (BoMIC): A Prospective Study. *Lancet Diabetes Endocrinol* (2019) 7(1):34-43. doi: 10.1016/S2213-8587(18)30308-5

149. Tsai JN, Uihlein AV, Burnett-Bowie SM, Neer RM, Derrico NP, Lee H, et al. Effects of Two Years of Teriparatide, Denosumab, or Both on Bone Microarchitecture and Strength (DATA-HRpQCT Study). *J Clin Endocrinol Metab* (2016) 101(5):2023-30. doi: 10.1210/jc.2016-1160

150. Feldkamp LA, Goldstein SA, Parfitt MA, Jesion G, Kleerekoper M. The Direct Examination of Three-Dimensional Bone Architecture *In Vitro* by Computed Tomography. *J Bone Miner Res* (1989) 4(1):3-11. doi: 10.1002/jbmr.5650040103

151. Campbell GM, Sophocleous A. Quantitative Analysis of Bone and Soft Tissue by Micro-Computed Tomography: Applications to Ex Vivo and In Vivo Studies. *Bonekey Rep* (2014) 3:564. doi: 10.1038/bonekey.2014.59

152. Ozan F, Pekedis M, Koyuncu Ş, Altay T, Yildiz H, Kayali C. Micro-Computed Tomography and Mechanical Evaluation of Trabecular Bone Structure in Osteopenic and Osteoporotic Fractures. *J Orthop Surg* (2017) 25 (1):230949901769271. doi: 10.1177/2309499017692718

153. Perilli E, Parkinson IH, Reynolds KJ. Micro-CT Examination of Human Bone: From Biopsies Towards the Entire Organ. *Ann Ist Super Sanita* (2012) 48(1):75-82. doi: 10.4415/ANN_12_01_13

154. Jiang Y, Zhao J, Liao EY, Dai RC, Wu XP, Genant HK. Application of Micro-Ct Assessment of 3-D Bone Microstructure in Preclinical and Clinical Studies. *J Bone Mineral Metab* (2005) 23:122-31. doi: 10.1007/BF03026336

155. Burghardt AJ, Link TM, Majumdar S. High-Resolution Computed Tomography for Clinical Imaging of Bone Microarchitecture. *Clin Orthopaedics Related Res* (2011) 469(8):2179-93. doi: 10.1007/s11999-010-1766-x

156. De Bournonville S, Vangrunderbeeck S, Kerckhofs G. Contrast-Enhanced microCT for Virtual 3D Anatomical Pathology of Biological Tissues: A Literature Review. *Contrast Media Mol Imaging* (2019) 2019:8617406. doi: 10.1155/2019/8617406

157. Tratwal J, Labella R, Bravenboer N, Kerckhofs G, Douni E, Scheller EL, et al. Reporting Guidelines, Review of Methodological Standards, and Challenges Toward Harmonization in Bone Marrow Adiposity Research. Report of the Methodologies Working Group of the International Bone Marrow Adiposity Society. *Front Endocrinol* (2020) 11:65. doi: 10.3389/fendo.2020.00065

158. Dall'Ara E, Boudiffa M, Taylor C, Schug D, Fiegle E, Kennerley AJ, et al. Longitudinal Imaging of the Ageing Mouse. *Mech Ageing Dev* (2016) 160:93-116. doi: 10.1016/j.mad.2016.08.001

159. Bouxsein ML, Boyd SK, Christiansen BA, Guldberg RE, Jepsen KJ, Müller R. Guidelines for Assessment of Bone Microstructure in Rodents Using Micro-Computed Tomography. *J Bone Mineral Res* (2010) 25:1468-86. doi: 10.1002/jbmr.141

160. Leitch VD, Brassill MJ, Rahman S, Butterfield NC, Ma P, Logan JG, et al. PYY is a Negative Regulator of Bone Mass and Strength. *Bone* (2019) 127:427-35. doi: 10.1016/j.bone.2019.07.011
and Potential Pleiotropic Effects on Bone. Gibson G, Editor. *PloS Genet* (2010) 6(11):e1001217. doi: 10.1371/journal.pgen.1001217

126. Paternoster L, Lorentzon M, Lehtimäki T, Eriksson J, Kähönen M, Raitakari O, et al. Genetic Determinants of Trabecular and Cortical Volumetric Bone Mineral Densities and Bone Microstructure. *Richards JB, Editor. PloS Genet* (2013) 9(2):e1003247. doi: 10.1371/journal.pgen.1003247

127. Zheng H-F, Tobias JH, Duncan E, Evans DM, Eriksson J, Paternoster L, et al. WNT16 Influences Bone Mineral Density, Cortical Bone Thickness, Bone Strength, and Osteoporotic Fracture Risk. *Gibson G, Editor. PloS Genet* (2012) 8(7):e1002745. doi: 10.1371/journal.pgen.1002745

128. Compston J. Type 2 Diabetes Mellitus and Bone. *J Intern Med* (2018) 283 (2):140-53. doi: 10.1111/joim.12725

129. Gasser JA, Willnecker J. Bone Measurements by Peripheral Quantitative Computed Tomography in Rodents. *Methods Mol Biol* (2019) 1914:533-58. doi: 10.1007/978-1-4939-8997-3_29

130. Schmidt C, Priemel M, Kohler T, Weusten A, Müller R, Amling M, et al. Precision and Accuracy of Peripheral Quantitative Computed Tomography (pQCT) in the Mouse Skeleton Compared With Histology and Microcomputed Tomography (µct). *J Bone Miner Res* (2003) 18(8):1486-96. doi: 10.1359/jbmr.2003.18.8.1486

131. Brodt MD, Pelz GB, Taniguchi J, Silva MJ. Accuracy of Peripheral Quantitative Computed Tomography (pQCT) for Assessing Area and Density of Mouse Cortical Bone. *Calcif Tissue Int* (2003) 73(4):411-8. doi: 10.1007/s00223-002-0006-0

132. Manske SL, Zhu Y, Sandino C, Boyd SK. Human Trabecular Bone Microarchitecture can be Assessed Independently of Density With Second Generation HR-pQCT. *Bone* (2015) 79:213-21. doi: 10.1016/j.bone.2015.06.006

133. Whittier DE, Boyd SK, Burghardt AJ, Paccou J, Ghasem-Zadeh A, Chapurlat R, et al. Guidelines for the Assessment of Bone Density and Microarchitecture *In Vivo* Using High-Resolution Peripheral Quantitative Computed Tomography. *Osteoporos Int* (2020) 31(9):1607-27. doi: 10.1007/s00198-020-05438-5

134. Boutroy S, Bouxsein ML, Munoz F, Delmas PD. *In Vivo* Assessment of Trabecular Bone Microarchitecture by High-Resolution Peripheral Quantitative Computed Tomography. *J Clin Endocrinol Metab* (2005) 90 (12):6508-15. doi: 10.1210/jc.2005-1258

135. Laib A, Hauselmann HJ, Ruegsegger P. *In Vivo* High Resolution 3D-QCT of the Human Forearm. *Technol Heal Care* (1998) 6(5-6):329-37. doi: 10.3233/THC-1998-65-606

136. Shanbhogue VV, Brixen K, Hansen S. Age- and Sex-Related Changes in Bone Microarchitecture and Estimated Strength: A Three-Year Prospective Study Using HRpQCT. *J Bone Miner Res* (2016) 31(8):1541-9. doi: 10.1002/jbmr.2817

137. Hamilton EJ, Ghasem-Zadeh A, Gianatti E, Lim-Joon D, Bolton D, Zebaze R, et al. Structural Decay of Bone Microarchitecture in Men With Prostate Cancer Treated With Androgen Deprivation Therapy. *J Clin Endocrinol Metab* (2010) 95(12):E456-63. doi: 10.1210/jc.2010-0902

138. van Rietbergen B, Ito K. A Survey of Micro-Finite Element Analysis for Clinical Assessment of Bone Strength: The First Decade. *J Biomech* (2015) 48 (5):832-41. doi: 10.1016/j.jbiomech.2014.12.024

139. Whittier DE, Manske SL, Kiel DP, Bouxsein M, Boyd SK. Harmonizing Finite Element Modelling for non-Invasive Strength Estimation by High-Resolution Peripheral Quantitative Computed Tomography. *J Biomech* (2018) 80:63-71. doi: 10.1016/j.jbiomech.2018.08.030

140. Kroker A, Zhu Y, Manske SL, Barber R, Mohtadi N, Boyd SK. Quantitative *In Vivo* Assessment of Bone Microarchitecture in the Human Knee Using HR-pQCT. *Bone* (2017) 97:43-8. doi: 10.1016/j.bone.2016.12.015

141. Sada K, Chiba K, Kajiyama S, Okazaki N, Yonekura A, Tomita M, et al. Bone Mineral Density and Microstructure of the Elbow in Baseball Pitchers: An Analysis by Second-Generation HR-pQCT. *J Clin Densitom* (2020) 23 (2):322-8. doi: 10.1016/j.jocd.2019.03.001

142. Zebaze R, Seeman E. Cortical Bone: A Challenging Geography. *J Bone Miner Res* (2015) 30(1):24-9. doi: 10.1002/jbmr.2419

143. Nishiyama KK, Macdonald HM, Buie HR, Hanley DA, Boyd SK. Postmenopausal Women With Osteopenia Have Higher Cortical Porosity and Thinner Cortices at the Distal Radius and Tibia Than Women With

Normal aBMD: An *In Vivo* HR-pQCT Study. *J Bone Miner Res* (2009) 25 (4):901019190442039-30. doi: 10.1359/jbmr.091020

144. Burghardt AJ, Buie HR, Laib A, Majumdar S, Boyd SK. Reproducibility of Direct Quantitative Measures of Cortical Bone Microarchitecture of the Distal Radius and Tibia by HR-pQCT. *Bone* (2010) 47(3):519-28. doi: 10.1016/j.bone.2010.05.034

145. Zebaze R, Ghasem-Zadeh A, Mbala A, Seeman E. A New Method of Segmentation of Compact-Appearing, Transitional and Trabecular Compartments and Quantification of Cortical Porosity From High Resolution Peripheral Quantitative Computed Tomographic Images. *Bone* (2013) 54(1):8-20. doi: 10.1016/j.bone.2013.01.007

146. Burghardt AJ, Pialat JB, Kazakia GJ, Boutroy S, Engelke K, Patsch JM, et al. Multicenter Precision of Cortical and Trabecular Bone Quality Measures Assessed by High-Resolution Peripheral Quantitative Computed Tomography. *J Bone Miner Res* (2013) 28(3):524-36. doi: 10.1002/jbmr.1795

147. Piot A, Chapurlat RD, Claustrat B, Szulc P. Relationship Between Sex Steroids and Deterioration of Bone Microarchitecture in Older Men: The Prospective STRAMBO Study. *J Bone Miner Res* (2019) 34(9):1562-73. doi: 10.1002/jbmr.3746

148. Samelson EJ, Broe KE, Xu H, Yang L, Boyd S, Biver E, et al. Cortical and Trabecular Bone Microarchitecture as an Independent Predictor of Incident Fracture Risk in Older Women and Men in the Bone Microarchitecture International Consortium (BoMIC): A Prospective Study. *Lancet Diabetes Endocrinol* (2019) 7(1):34-43. doi: 10.1016/S2213-8587(18)30308-5

149. Tsai JN, Uihlein AV, Burnett-Bowie SM, Neer RM, Derrico NP, Lee H, et al. Effects of Two Years of Teriparatide, Denosumab, or Both on Bone Microarchitecture and Strength (DATA-HRpQCT Study). *J Clin Endocrinol Metab* (2016) 101(5):2023-30. doi: 10.1210/jc.2016-1160

150. Feldkamp LA, Goldstein SA, Parfitt MA, Jesion G, Kleerekoper M. The Direct Examination of Three-Dimensional Bone Architecture *In Vitro* by Computed Tomography. *J Bone Miner Res* (1989) 4(1):3-11. doi: 10.1002/jbmr.5650040103

151. Campbell GM, Sophocleous A. Quantitative Analysis of Bone and Soft Tissue by Micro-Computed Tomography: Applications to Ex Vivo and In Vivo Studies. *Bonekey Rep* (2014) 3:564. doi: 10.1038/bonekey.2014.59

152. Ozan F, Pekedis M, Koyuncu Ş, Altay T, Yildiz H, Kayali C. Micro-Computed Tomography and Mechanical Evaluation of Trabecular Bone Structure in Osteopenic and Osteoporotic Fractures. *J Orthop Surg* (2017) 25 (1):230949901769271. doi: 10.1177/2309499017692718

153. Perilli E, Parkinson IH, Reynolds KJ. Micro-CT Examination of Human Bone: From Biopsies Towards the Entire Organ. *Ann Ist Super Sanita* (2012) 48(1):75-82. doi: 10.4415/ANN_12_01_13

154. Jiang Y, Zhao J, Liao EY, Dai RC, Wu XP, Genant HK. Application of Micro-Ct Assessment of 3-D Bone Microstructure in Preclinical and Clinical Studies. *J Bone Mineral Metab* (2005) 23:122-31. doi: 10.1007/BF03026336

155. Burghardt AJ, Link TM, Majumdar S. High-Resolution Computed Tomography for Clinical Imaging of Bone Microarchitecture. *Clin Orthopaedics Related Res* (2011) 469(8):2179-93. doi: 10.1007/s11999-010-1766-x

156. De Bournonville S, Vangrunderbeeck S, Kerckhofs G. Contrast-Enhanced microCT for Virtual 3D Anatomical Pathology of Biological Tissues: A Literature Review. *Contrast Media Mol Imaging* (2019) 2019:8617406. doi: 10.1155/2019/8617406

157. Tratwal J, Labella R, Bravenboer N, Kerckhofs G, Douni E, Scheller EL, et al. Reporting Guidelines, Review of Methodological Standards, and Challenges Toward Harmonization in Bone Marrow Adiposity Research. Report of the Methodologies Working Group of the International Bone Marrow Adiposity Society. *Front Endocrinol* (2020) 11:65. doi: 10.3389/fendo.2020.00065

158. Dall'Ara E, Boudiffa M, Taylor C, Schug D, Fiegle E, Kennerley AJ, et al. Longitudinal Imaging of the Ageing Mouse. *Mech Ageing Dev* (2016) 160:93-116. doi: 10.1016/j.mad.2016.08.001

159. Bouxsein ML, Boyd SK, Christiansen BA, Guldberg RE, Jepsen KJ, Müller R. Guidelines for Assessment of Bone Microstructure in Rodents Using Micro-Computed Tomography. *J Bone Mineral Res* (2010) 25:1468-86. doi: 10.1002/jbmr.141

160. Leitch VD, Brassill MJ, Rahman S, Butterfield NC, Ma P, Logan JG, et al. PYY is a Negative Regulator of Bone Mass and Strength. *Bone* (2019) 127:427-35. doi: 10.1016/j.bone.2019.07.011
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179. Koth J, Maguire ML, McClymont D, Diffley L, Thornton VL, Beech J, et al. High-Resolution Magnetic Resonance Imaging of the Regenerating Adult Zebrafish Heart. *Sci Rep* (2017) 7(1):2917. doi: 10.1038/s41598-017- 03050-y

180. Merrifield GD, Mullin J, Gallagher L, Tucker C, Jansen MA, Denvir M, et al. Rapid and Recoverable *In Vivo* Magnetic Resonance Imaging of the Adult Zebrafish at 7T. *Magn Reson Imaging* (2017) 37:9-15. doi: 10.1016/ j.mri.2016.10.013

181. Recker RR, Moreira CA. *Bone Histomorphometry in Clinical Practice*. (2018). pp. 310-318. doi: 10.1002/9781119266594.ch39

182. Chappard D, Baslé MF, Legrand E, Audran M. New Laboratory Tools in the Assessment of Bone Quality. *Osteoporosis Int* (2011) 22:2225-40. doi: 10.1007/s00198-011-1573-6

183. Compston J. Bone Histomorphometry. In: *Methods in Bone Biology*. Boston, MA: Springer (2007). p. 177-97.

184. Slyfield CR, Tkachenko EV, Wilson DL, Hernandez CJ. Three-Dimensional Dynamic Bone Histomorphometry. *J Bone Miner Res* (2012) 27(2):486-95. doi: 10.1002/jbmr.553

185. Malhan D, Muelke M, Rosch S, Schaefer AB, Merboth F, Weisweiler D, et al. An Optimized Approach to Perform Bone Histomorphometry. *Front Endocrinol* (Lausanne) (2018) 9:666. doi: 10.3389/fendo.2018.00666

186. Dempster DW, Compston JE, Drezner MK, Glorieux FH, Kanis JA, Malluche H, et al. Standardized Nomenclature, Symbols, and Units for Bone Histomorphometry: A 2012 Update of the Report of the ASBMR Histomorphometry Nomenclature Committee. *J Bone Mineral Res* (2013) 28:2-17. doi: 10.1002/jbmr.1805

187. Parfitt AM, Drezner MK, Glorieux FH, Kanis JA, Malluche H, Meunier PJ, et al. Bone Histomorphometry: Standardization of Nomenclature, Symbols, and Units: Report of the Asbmr Histomorphometry Nomenclature Committee. *J Bone Miner Res* (1987) 2(6):595-610. doi: 10.1002/ jbmr.5650020617

188. Glorieux FH, Travers R, Taylor A, Bowen JR, Rauch F, Norman M, et al. Normative Data for Iliac Bone Histomorphometry in Growing Children. *Bone* (2000) 26(2):103-9. doi: 10.1016/S8756-3282(99)00257-4

189. Rauch F, Travers R, Parfitt A, Glorieux F. Static and Dynamic Bone Histomorphometry in Children With Osteogenesis Imperfecta. *Bone* (2000) 26(6):581-9. doi: 10.1016/S8756-3282(00)00269-6

190. Amling M, Pösl M, Ritzel H, Hahn M, Vogel M, Wening VJ, et al. Architecture and Distribution of Cancellous Bone Yield Vertebral Fracture Clues. A Histomorphometric Analysis of the Complete Spinal Column From 40 Autopsy Specimens. *Arch Orthop Trauma Surg* (1996) 115(5):262-9. doi: 10.1007/BF00439050

191. Erben RG, Glösmann M. Histomorphometry in Rodents. *Methods Mol Biol* (2019) 1914:411-35. doi: 10.1007/978-1-4939-8997-3_24

192. Bassett JHD, Boyde A, Howell PGT, Bassett RH, Galliford TM, Archanco M, et al. Optimal Bone Strength and Mineralization Requires the Type 2 Iodothyronine Deiodinase in Osteoblasts. *Proc Natl Acad Sci USA* (2010) 107(16):7604-9. doi: 10.1073/pnas.0911346107

193. Dion N, Fortin A, Ste-Marie L-G. Methods in Bone Histomorphometry for Animal Models. *Osteoporos Res* (2011) 37-43. doi: 10.1007/978-0-85729-293-3_4

194. Jun Du S, Frenkel V, Zohar Y, Kindschi G. Visualizing Normal and Defective Bone Development in Zebrafish Embryos Using the Fluorescent Chromophore Calcein. *Dev Biol* (2001) 238(2):239-46. doi: 10.1006/ dbio.2001.0390

195. Kimmel CB, DeLaurier A, Ullmann B, Dowd J, McFadden M. Modes of Developmental Outgrowth and Shaping of a Craniofacial Bone in Zebrafish. *PloS One* (2010) 5(3):e9475. doi: 10.1371/journal.pone.0009475

196. Recidoro AM, Roof AC, Schmitt M, Worton LE, Petrie T, Strand N, et al. Botulinum Toxin Induces Muscle Paralysis and Inhibits Bone Regeneration in Zebrafish. *J Bone Miner Res* (2014) 29(11):2346-56. doi: 10.1002/ jbmr.2274

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183. Compston J. Bone Histomorphometry. In: *Methods in Bone Biology*. Boston, MA: Springer (2007). p. 177-97.

184. Slyfield CR, Tkachenko EV, Wilson DL, Hernandez CJ. Three-Dimensional Dynamic Bone Histomorphometry. *J Bone Miner Res* (2012) 27(2):486-95. doi: 10.1002/jbmr.553

185. Malhan D, Muelke M, Rosch S, Schaefer AB, Merboth F, Weisweiler D, et al. An Optimized Approach to Perform Bone Histomorphometry. *Front Endocrinol* (Lausanne) (2018) 9:666. doi: 10.3389/fendo.2018.00666

186. Dempster DW, Compston JE, Drezner MK, Glorieux FH, Kanis JA, Malluche H, et al. Standardized Nomenclature, Symbols, and Units for Bone Histomorphometry: A 2012 Update of the Report of the ASBMR Histomorphometry Nomenclature Committee. *J Bone Mineral Res* (2013) 28:2-17. doi: 10.1002/jbmr.1805

187. Parfitt AM, Drezner MK, Glorieux FH, Kanis JA, Malluche H, Meunier PJ, et al. Bone Histomorphometry: Standardization of Nomenclature, Symbols, and Units: Report of the Asbmr Histomorphometry Nomenclature Committee. *J Bone Miner Res* (1987) 2(6):595-610. doi: 10.1002/ jbmr.5650020617

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189. Rauch F, Travers R, Parfitt A, Glorieux F. Static and Dynamic Bone Histomorphometry in Children With Osteogenesis Imperfecta. *Bone* (2000) 26(6):581-9. doi: 10.1016/S8756-3282(00)00269-6

190. Amling M, Pösl M, Ritzel H, Hahn M, Vogel M, Wening VJ, et al. Architecture and Distribution of Cancellous Bone Yield Vertebral Fracture Clues. A Histomorphometric Analysis of the Complete Spinal Column From 40 Autopsy Specimens. *Arch Orthop Trauma Surg* (1996) 115(5):262-9. doi: 10.1007/BF00439050

191. Erben RG, Glösmann M. Histomorphometry in Rodents. *Methods Mol Biol* (2019) 1914:411-35. doi: 10.1007/978-1-4939-8997-3_24

192. Bassett JHD, Boyde A, Howell PGT, Bassett RH, Galliford TM, Archanco M, et al. Optimal Bone Strength and Mineralization Requires the Type 2 Iodothyronine Deiodinase in Osteoblasts. *Proc Natl Acad Sci USA* (2010) 107(16):7604-9. doi: 10.1073/pnas.0911346107

193. Dion N, Fortin A, Ste-Marie L-G. Methods in Bone Histomorphometry for Animal Models. *Osteoporos Res* (2011) 37-43. doi: 10.1007/978-0-85729-293-3_4

194. Jun Du S, Frenkel V, Zohar Y, Kindschi G. Visualizing Normal and Defective Bone Development in Zebrafish Embryos Using the Fluorescent Chromophore Calcein. *Dev Biol* (2001) 238(2):239-46. doi: 10.1006/ dbio.2001.0390

195. Kimmel CB, DeLaurier A, Ullmann B, Dowd J, McFadden M. Modes of Developmental Outgrowth and Shaping of a Craniofacial Bone in Zebrafish. *PloS One* (2010) 5(3):e9475. doi: 10.1371/journal.pone.0009475

196. Recidoro AM, Roof AC, Schmitt M, Worton LE, Petrie T, Strand N, et al. Botulinum Toxin Induces Muscle Paralysis and Inhibits Bone Regeneration in Zebrafish. *J Bone Miner Res* (2014) 29(11):2346-56. doi: 10.1002/ jbmr.2274

197. Inohaya K, Takano Y, Kudo A. The Teleost Intervertebral Region Acts as a Growth Center of the Centrum: *In Vivo* Visualization of Osteoblasts and Their Progenitors in Transgenic Fish. *Dev Dyn* (2007) 236(11):3031-46. doi: 10.1002/dvdy.21329

198. Edsall SC, Franz-Odendaal TA. A Quick Whole-Mount Staining Protocol for Bone Deposition and Resorption. *Zebrafish* (2010) 7(3):275-80. doi: 10.1089/ zeb.2009.0641

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242. Reid IR. Vitamin D Effect on Bone Mineral Density and Fractures. *Endocrinol Metab Clinics North America* (2017) 46:935-45. doi: 10.1016/j.ecl.2017.07.005

243. Bischoff-Ferrari HA. Influence of Vitamin D on Fracture Reduction Among Older Adults: A Discussion of Recent Meta-Analysis Findings. *Osteologie* (2019) 28(2):136-9. doi: 10.1055/a-0861-2813

244. Munns CF, Shaw N, Kiely M, Specker BL, Thacher TD, Ozono K, et al. Global Consensus Recommendations on Prevention and Management of Nutritional Rickets. *J Clin Endocrinol Metab* (2016) 101(2):394-415. doi: 10.1210/jc.2015-2175

245. Cherniack EP, Troen BR. Calciotropic Hormones. In: *Osteoporosis in Older Persons: Advances in Pathophysiology and Therapeutic Approaches, 2nd ed.* Springer International Publishing (2016). p. 43-58.

246. Tiosano D, Hochberg Z. Hypophosphatemia: The Common Denominator of All Rickets. *J Bone Mineral Metab* (2009) 27:392-401. doi: 10.1007/s00774-009-0079-1

247. Cavalier E, Souberbielle JC. Vitamin D and Its Metabolites: From Now and Beyond. *EJIFCC* (2018) 29(2):105-10.

248. Uday S, Högler W. Spot the Silent Sufferers: A Call for Clinical Diagnostic Criteria for Solar and Nutritional Osteomalacia. *J Steroid Biochem Mol Biol* (2019) 188:141-6. doi: 10.1016/j.jsmb.2019.01.004

249. Seldeen KL, Pang M, Rodriguez-Gonzalez M, Hernandez M, Sheridan Z, Yu P, et al. A Mouse Model of Vitamin D Insufficiency: Is There a Relationship Between 25(OH) Vitamin D Levels and Obesity? *Nutr Metab* (2017) 14 (1):26. doi: 10.1186/s12986-017-0174-6

250. Szulc P, Naylor K, Hoyle NR, Eastell R, Leary ET. Use of CTX-I and PINP as Bone Turnover Markers: National Bone Health Alliance Recommendations to Standardize Sample Handling and Patient Preparation to Reduce Pre-Analytical Variability. *Osteoporos Int* (2017) 28(9):2541-56. doi: 10.1007/s00198-017-4082-4

251. Shaw N, Högler W. Biochemical Markers of Bone Metabolism. In: Glorieux FH, Pettifor JM, Jüppner H. *Pediatric Bone*. Elsevier Inc (2012). p. 361-81. doi: 10.1016/B978-0-12-382040-2.10015-2

252. Guanabens N, Parés A, Álvarez L, De Osaba MJM, Monegal A, Peris P, et al. Collagen-Related Markers of Bone Turnover Reflect the Severity of Liver Fibrosis in Patients With Primary Biliary Cirrhosis. *J Bone Miner Res* (1998) 13(4):731-8. doi: 10.1359/jbmr.1998.13.4.731

253. Delmas PD, Demiaux B, Malaval L, Chapuy MC, Edouard C, Meunier PJ. Serum Bone Gamma Carboxyglutamic Acid-Containing Protein in Primary Hyperparathyroidism and in Malignant Hypercalcemia. Comparison With Bone Histomorphometry. *J Clin Invest* (1986) 77(3):985-91. doi: 10.1172/JCI121400

254. Uebelhart D, Gineyts E, Chapuy M-C, Delmas PD. Urinary Excretion of Pyridinium Crosslinks: A New Marker of Bone Resorption in Metabolic Bone Disease. *Bone Miner* (1990) 8(1):87-96. doi: 10.1016/0169-6009(91) 90143-N

255. Eisman JA, Bone HG, Hosking DJ, McClung MR, Reid IR, Rizzoli R, et al. Odanacatib in the Treatment of Postmenopausal Women With Low Bone Mineral Density: Three-Year Continued Therapy and Resolution of Effect. *J Bone Miner Res* (2011) 26(2):242-51. doi: 10.1002/jbmr.212

256. Imel EA, Liu Z, Acton D, Coffman M, Gebregziabher N, Tong Y, et al. Interferon Gamma-1b Does Not Increase Markers of Bone Resorption in Autosomal Dominant Osteopetrosis. *J Bone Miner Res* (2019) 34(8):1436-45. doi: 10.1002/jbmr.3715

257. Vasikaran S, Cooper C, Eastell R, Griesmacher A, Morris HA, Trenti T, et al. International Osteoporosis Foundation and International Federation of Clinical Chemistry and Laboratory Medicine Position on Bone Marker Standards in Osteoporosis. *Clin Chem Lab Med* (2011) 49(8):1271-4. doi: 10.1515/CCLM.2011.602

258. Szulc P, Delmas PD. Biochemical Markers of Bone Turnover in Men. *Calcified Tissue Int* (2001) 69(4):229-34. doi: 10.1007/s00223-001-1059-1

259. Naylor KE, Jacques RM, Paggiosi M, Gossiel F, Peel NFA, McCloskey EV, et al. Response of Bone Turnover Markers to Three Oral Bisphosphonate Therapies in Postmenopausal Osteoporosis: The TRIO Study. *Osteoporos Int* (2016) 27(1):21-31. doi: 10.1007/s00198-015-3145-7

260. Stepan JJ. Prediction of Bone Loss in Postmenopausal Women. *Osteoporosis Int* (2000) 11:S45-54. doi: 10.1007/s00198007005

261. Vilaca T, Gossiel F, Eastell R. Bone Turnover Markers: Use in Fracture Prediction. *J Clin Densitom* (2017) 20(3):346-52. doi: 10.1016/j.jodc.2017.06.020

262. Eastell R, Pigott T, Gossiel F, Naylor KE, Walsh JS, APeel NF. Bone Turnover Markers: Are They Clinically Useful? *Eur J Endocrinol* (2018) 178:R19-31. doi: 10.1530/EJE-17-0585

263. *A Practical Approach to Adolescent Bone Health. A Practical Approach to Adolescent Bone Health*. Springer International Publishing (2018). doi: 10.1007/978-3-319-72880-3

264. Redmond J, Fulford AJ, Jarjou L, Zhou B, Prentice A, Schoenmakers I. Diurnal Rhythms of Bone Turnover Markers in Three Ethnic Groups. *J Clin Endocrinol Metab* (2016) 101(8):3222-30. doi: 10.1210/jc.2016-1183

265. Christgau S, Bitsch-Jensen O, Hanover Bjarnason N, Gamwell Henriksen E, Qvist P, Alexandersen P, et al. Serum CrossLaps for Monitoring the Response in Individuals Undergoing Antiresorptive Therapy. *Bone* (2000) 26(5):505-11. doi: 10.1016/S8756-3282(00)00248-9

266. Rauchenzauner M, Schmid A, Heinz-Erian P, Kapelari K, Falkensammer G, Griesmacher A, et al. Sex- and Age-Specific Reference Curves for Serum Markers of Bone Turnover in Healthy Children From 2 Months to 18 Years. *J Clin Endocrinol Metab* (2007) 92(2):443-9. doi: 10.1210/jc.2006-1706

267. Choi JJ, Cho HH. Effects of Di(2-Ethylhexyl)Phthalate on Bone Metabolism in Ovariectomized Mice. *J Bone Metab* (2019) 26(3):169-77. doi: 10.11005/jbm.2019.26.3.169

268. Kim TH, Kim HJ, Lee SH, Kim SY. Potent Inhibitory Effect of Foeniculum Vulgare Miller Extract on Osteoclast Differentiation and Ovariectomy-Induced Bone Loss. *Int J Mol Med* (2012) 29(6):1053-9. doi: 10.3892/ijmm.2012.950

269. Hammond CL, Moro E. Using Transgenic Reporters to Visualize Bone and Cartilage Signaling During Development In Vivo. *Front Endocrinol (Lausanne)* (2012) 3(JUL). doi: 10.3389/fendo.2012.00091

270. Baird DA, Evans DMDS, Kamanu FK, Gregory JS, Saunders FR, Giuraniu CV, et al. Identification of Novel Loci Associated With Hip Shape: A Meta-Analysis of Genomewide Association Studies. *J Bone Miner Res* (2019) 34 (2):241-51. doi: 10.1002/jbmr.3605

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273. Rad HS, Lam SCB, Magland JF, Ong H, Li C, Song HK, et al. Quantifying Cortical Bone Water *In Vivo* by Three-Dimensional Ultra-Short Echo-Time MRI. *NMR Biomed* (2011) 24(7):855-64. doi: 10.1002/nbm.1631

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240. Zang L, Shimada Y, Nishimura Y, Tanaka T, Nishimura N, Novel A. Reliable Method for Repeated Blood Collection From Aquarium Fish. *Zebrafish* (2013) 10(3):425-32. doi: 10.1089/zeb.2012.0862

241. Holick MF. The Vitamin D Deficiency Pandemic: Approaches for Diagnosis, Treatment and Prevention. *Rev Endocrine Metab Disord* (2017) 18:153-65. doi: 10.1007/s11154-017-9424-1

242. Reid IR. Vitamin D Effect on Bone Mineral Density and Fractures. *Endocrinol Metab Clinics North America* (2017) 46:935-45. doi: 10.1016/j.ecl.2017.07.005

243. Bischoff-Ferrari HA. Influence of Vitamin D on Fracture Reduction Among Older Adults: A Discussion of Recent Meta-Analysis Findings. *Osteologie* (2019) 28(2):136-9. doi: 10.1055/a-0861-2813

244. Munns CF, Shaw N, Kiely M, Specker BL, Thacher TD, Ozono K, et al. Global Consensus Recommendations on Prevention and Management of Nutritional Rickets. *J Clin Endocrinol Metab* (2016) 101(2):394-415. doi: 10.1210/jc.2015-2175

245. Cherniack EP, Troen BR. Calciotropic Hormones. In: *Osteoporosis in Older Persons: Advances in Pathophysiology and Therapeutic Approaches, 2nd ed.* Springer International Publishing (2016). p. 43-58.

246. Tiosano D, Hochberg Z. Hypophosphatemia: The Common Denominator of All Rickets. *J Bone Mineral Metab* (2009) 27:392-401. doi: 10.1007/s00774-009-0079-1

247. Cavalier E, Souberbielle JC. Vitamin D and Its Metabolites: From Now and Beyond. *EJIFCC* (2018) 29(2):105-10.

248. Uday S, Högler W. Spot the Silent Sufferers: A Call for Clinical Diagnostic Criteria for Solar and Nutritional Osteomalacia. *J Steroid Biochem Mol Biol* (2019) 188:141-6. doi: 10.1016/j.jsmbm.2019.01.004

249. Seldeen KL, Pang M, Rodriguez-Gonzalez M, Hernandez M, Sheridan Z, Yu P, et al. A Mouse Model of Vitamin D Insufficiency: Is There a Relationship Between 25(OH) Vitamin D Levels and Obesity? *Nutr Metab* (2017) 14 (1):26. doi: 10.1186/s12986-017-0174-6

250. Szulc P, Naylor K, Hoyle NR, Eastell R, Leary ET. Use of CTX-I and PINP as Bone Turnover Markers: National Bone Health Alliance Recommendations to Standardize Sample Handling and Patient Preparation to Reduce Pre-Analytical Variability. *Osteoporos Int* (2017) 28(9):2541-56. doi: 10.1007/s00198-017-4082-4

251. Shaw N, Högler W. Biochemical Markers of Bone Metabolism. In: Glorieux FH, Pettifor JM, Jüppner H. *Pediatric Bone*. Elsevier Inc (2012). p. 361-81. doi: 10.1016/B978-0-12-382040-2.10015-2

252. Guanabens N, Parés A, Álvarez L, De Osaba MJM, Monegal A, Peris P, et al. Collagen-Related Markers of Bone Turnover Reflect the Severity of Liver Fibrosis in Patients With Primary Biliary Cirrhosis. *J Bone Miner Res* (1998) 13(4):731-8. doi: 10.1359/jbmr.1998.13.4.731

253. Delmas PD, Demiaux B, Malaval L, Chapuy MC, Edouard C, Meunier PJ. Serum Bone Gamma Carboxyglutamic Acid-Containing Protein in Primary Hyperparathyroidism and in Malignant Hypercalcemia. Comparison With Bone Histomorphometry. *J Clin Invest* (1986) 77(3):985-91. doi: 10.1172/JCI121400

254. Uebelhart D, Gineyts E, Chapuy M-C, Delmas PD. Urinary Excretion of Pyridinium Crosslinks: A New Marker of Bone Resorption in Metabolic Bone Disease. *Bone Miner* (1990) 8(1):87-96. doi: 10.1016/0169-6009(91) 90143-N

255. Eisman JA, Bone HG, Hosking DJ, McClung MR, Reid IR, Rizzoli R, et al. Odanacatib in the Treatment of Postmenopausal Women With Low Bone Mineral Density: Three-Year Continued Therapy and Resolution of Effect. *J Bone Miner Res* (2011) 26(2):242-51. doi: 10.1002/jbmr.212

256. Imel EA, Liu Z, Acton D, Coffman M, Gebregziabher N, Tong Y, et al. Interferon Gamma-1b Does Not Increase Markers of Bone Resorption in Autosomal Dominant Osteopetrosis. *J Bone Miner Res* (2019) 34(8):1436-45. doi: 10.1002/jbmr.3715

257. Vasikaran S, Cooper C, Eastell R, Griesmacher A, Morris HA, Trenti T, et al. International Osteoporosis Foundation and International Federation of Clinical Chemistry and Laboratory Medicine Position on Bone Marker Standards in Osteoporosis. *Clin Chem Lab Med* (2011) 49(8):1271-4. doi: 10.1515/CCLM.2011.602

258. Szulc P, Delmas PD. Biochemical Markers of Bone Turnover in Men. *Calcified Tissue Int* (2001) 69(4):229-34. doi: 10.1007/s00223-001-1059-1

259. Naylor KE, Jacques RM, Paggiosi M, Gossiel F, Peel NFA, McCloskey EV, et al. Response of Bone Turnover Markers to Three Oral Bisphosphonate Therapies in Postmenopausal Osteoporosis: The TRIO Study. *Osteoporos Int* (2016) 27(1):21-31. doi: 10.1007/s00198-015-3145-7

260. Stepan JJ. Prediction of Bone Loss in Postmenopausal Women. *Osteoporosis Int* (2000) 11:S45-54. doi: 10.1007/s00198007005

261. Vilaca T, Gossiel F, Eastell R. Bone Turnover Markers: Use in Fracture Prediction. *J Clin Densitom* (2017) 20(3):346-52. doi: 10.1016/j.jodc.2017.06.020

262. Eastell R, Pigott T, Gossiel F, Naylor KE, Walsh JS, APeel NF. Bone Turnover Markers: Are They Clinically Useful? *Eur J Endocrinol* (2018) 178:R19-31. doi: 10.1530/EJE-17-0585

263. *A Practical Approach to Adolescent Bone Health. A Practical Approach to Adolescent Bone Health*. Springer International Publishing (2018). doi: 10.1007/978-3-319-72880-3

264. Redmond J, Fulford AJ, Jarjou L, Zhou B, Prentice A, Schoenmakers I. Diurnal Rhythms of Bone Turnover Markers in Three Ethnic Groups. *J Clin Endocrinol Metab* (2016) 101(8):3222-30. doi: 10.1210/jc.2016-1183

265. Christgau S, Bitsch-Jensen O, Hanover Bjarnason N, Gamwell Henriksen E, Qvist P, Alexandersen P, et al. Serum CrossLaps for Monitoring the Response in Individuals Undergoing Antiresorptive Therapy. *Bone* (2000) 26(5):505-11. doi: 10.1016/S8756-3282(00)00248-9

266. Rauchenzauner M, Schmid A, Heinz-Erian P, Kapelari K, Falkensammer G, Griesmacher A, et al. Sex- and Age-Specific Reference Curves for Serum Markers of Bone Turnover in Healthy Children From 2 Months to 18 Years. *J Clin Endocrinol Metab* (2007) 92(2):443-9. doi: 10.1210/jc.2006-1706

267. Choi JJ, Cho HH. Effects of Di(2-Ethylhexyl)Phthalate on Bone Metabolism in Ovariectomized Mice. *J Bone Metab* (2019) 26(3):169-77. doi: 10.11005/jbm.2019.26.3.169

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**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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