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Characterisation of peripheral bone mineral density in youth at risk of secondary osteoporosis - a preliminary insight.

Objectives: To describe peripheral long bone material and structural differences in youth at risk of secondary osteoporosis across disease-specific profiles. Methods: Upper- and lower limbs of children and adolescents were scanned at 4% distal and 66% mid-shaft sites using peripheral Quantitative Computed Tomography sub-categorised as (1) increased risk of secondary osteoporosis (neuromuscular disorders; chronic diseases; endocrine diseases; inborn errors of metabolism; iatrogenic conditions), (2) low motor competence and (3) non-affected controls. Results: Children with disease-specific profiles showed a range of bone deficits compared to the control group with these predominantly indicated for neuromuscular disorders, chronic diseases and low motor competence. Deficits between upper arm and lower leg long bone parameters were different for disease-specific profiles compared to the control group. Endocortical radius, muscle area, and mid-cortical ring density were not significantly different for any disease-specific profile compared to the control group for any bone sites. Conclusions: Neuromuscular disorders, chronic diseases and low motor competence have a strong correlation to bone health for appendicular bone parameters in youth, suggesting a critical mechanical loading influence which may differ specific to disease profile. As mechanical loading effects are observed in regional bone analyses, targeted exercise interventions to improve bone strength should be implemented to examine if this is effective in reducing the risk of secondary osteoporosis in youth.


Introduction
Growth during skeletal ontogenesis, and in particular peak bone mass accrual during adolescence, is essential to develop strong, robust and healthy bones for the rest of the life-span [1][2] . Between 20-40% is determined by lifestyle factors, with exercise and physical activity of particular importance when optimising the contribution of mechanical loading to bone strength development 3 . A lack of bone accrual during development can increase the risk of developing osteoporosis 4,5 . Chronic conditions such as neuromuscular disorders, endocrine diseases, inborn errors of metabolism, and iatrogenic conditions are associated with compromised bone accrual, and increased risk of secondary forms of osteoporosis 4 . Recent evidence also suggests a relationship between movement disorders (low motor competence) and poorer bone health outcomes 6-8 .
Osteoporosis in adults is clinically presented by characteristic changes in cortical and trabecular bone architecture 9 . These can be assessed using peripheral quantitative computer tomography (pQCT). To our knowledge there are no studies which identify diseasespecific bone phenotypes and whether a particular bone site is affected by altered health status, disease categories, muscle disorders or motor impairment. Globally, fracture differences between limbs in healthy childhood populations have been researched, however an examination of sitespecific peripheral long-bone characteristics has not been undertaken [10][11][12][13] . The aim of this study was to determine if there were disease-specific differences in appendicular regional bone parameters in youth at risk of secondary osteoporosis through established peripheral Quantitative Computed Tomography (pQCT) profiles.

Study design
This is a cross sectional observational study looking at peripheral quantitative computed tomography data obtained from three groups. Data for key disease groups was obtained from presentations to Princess Margaret Hospital, Western Australia, with ethics approval obtained from the Human Research Ethics Committee (HREC; GEKO Quality Activity 12902). Data for the low motor competence and control group comprised of a re-analysis of previously published AMPitup (described below) and Griffith University Bone Densitometry Research Laboratory datasets 6,7,14-20 . HREC approval relevant to these datasets were also previously obtained (AMPitup: University of Notre Dame Australia HREC Reference 011004F, 09004F, 09050F, 09039F; and Griffith University HREC Reference PES/25/11/HREC; PES/12/05/HREC, PES/09/09/HREC), with written informed consent provided.

Group 2: Low motor competence
The "AMPitup" data set was collected at the University of Notre Dame Australia, Fremantle, Australia, from the AMPitup Program (ongoing exercise clinic), collected from individuals aged from 12 to 18 years old (n=51; male=35; female=17). Program eligibility included a formal DSM-V diagnosis of Developmental Coordination Disorder 21 or as assessment of low motor competence. The McCarron Assessment of Neuromuscular Development (MAND) was used to screen each participant's motor performance with a Neuromuscular Development Index (NDI) value of 85 or lower (≤1 SD) (mild motor disability) and/or a history of movement difficulties which included slowness, poor coordination, clumsiness, or poor motor skills, that impact everyday activities including daily living, school, and leisure classified as low motor competence 22,23 . This study only provides the baseline data, prior to the initiation of exercise. A participant was ineligible if they reported significant intellectual, neurological or physical disabilities. The pQCT scans were performed at PMH. Some of the presented data has been previously reported 6,7,24 . Table 1. Description of disease profiles included in each disease classification 4 additional to low motor competence 21 . n=sample size range for each group.
was found for the fibula 66% site by determining the average of the concentric rings from Division 7 to Division 9.

Statistical analysis
Statistical analyses were conducted with SPSS (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.), using a statistical significance set at p<0.05. Variables were described using mean, standard deviation (SD), and 95% confidence intervals (CI). Any participant with data missing in any of the scanned variables, or with a motion artefact graded four or five 24,29 was excluded from analysis. Only the first acceptable presentation (based on the earliest presentation date) of an individual was selected for analysis in this study 24,29 . A General Linear Model (GLM), with Bonferroni adjustment for multiple comparisons, was performed and is considered an appropriate statistical method to deal with confounding factors 30,31 . Fixed factors included age, bone length, sex and the group classification. A quadratic term for age (age-squared) was also added as a fixed effect to model the effect of growth 31 and has been shown to minimize the effect of growth 32 . Each bone measure was used as the dependent variable for each GLM test. Each model's residuals were checked and did not violate the assumption for GLM.

Results
Sample size varied for muscle and bone measures and across groups. Supplementary Tables 1 to 8 summarise means, medians, confidence intervals, and significant differences for the eight bone sites (4% and 66% of the fibula, tibia, ulna and radius) across the seven groups (control; neuromuscular disorders; chronic diseases; endocrine diseases; inborn errors of metabolism; iatrogenic diseases; and low motor competence) and sixteen variables (age; bone length; CoD; CoA; SSI; ToA; BSI; MuD; MuA; subcutaneous fat area; fat percentage; mid-cortical ring density; endocortical radius; pericortical radius; trabecular density; and midcortical density).

Overall group differences
GLM analyses reported significant group differences for 64.7% of all muscle and bone measures (44 of 68 variables) across the upper and lower limbs in one or more clinical conditions when compared to healthy controls, as outlined in Table 2 and Table 3. In particular neuromuscular disorders were significantly different from healthy controls for 52.9% of muscle and bone measures (CoD; CoA; SSI; ToA; BSI; trabecular density; MuD; subcutaneous fat area; fat percentage; pericortical radius) at numerous bone sites, with all but four measures being significantly lower (subcutaneous fat area and fat percentage at the Radius 66% site and Tibia 66% site were significantly greater). Participants with chronic diseases, endocrine diseases and low motor competence were the only other groups differing significantly from healthy controls. Chronic diseases were significantly lower than healthy controls for CoD, CoA, SSI, BSI, and trabecular density. Most bone characteristics at the Tibia 4% site of the endocrine diseases group were lower than those of healthy controls. Individuals with decreased motor competence were significantly lower than healthy controls at diaphyseal bone sites for CoA, SSI, ToA, BSI, and pericortical radius, and were significantly higher for subcutaneous fat area and fat percentage at the Radius 66% site and Tibia 66% site. No significant group differences compared to healthy controls were reported for inborn errors of metabolism and iatrogenic groups, however the very small samples sizes should be noted for these two groups.

Site specific findings
Group differences were significant for cortical area (lower in six sites across neuromuscular disorders, chronic Table 2. General Linear Model (GLM) differences in upper limb regional bone parameters to the control group (p-values). All significant values bolded with (*) if significantly lower than control, or (^) if significantly higher than control. n=sample sizes. diseases, endocrine diseases and low motor competence), trabecular density (lower at 4% tibia and fibular sites for neuromuscular, chronic and endocrine diseases), muscle density (lower across neuromuscular disorders, chronic diseases and low motor competence), subcutaneous fat area and fat percentage (higher in neuromuscular disorders, chronic diseases and low motor competence), as illustrated in Table 2 and Table 3. Even though cortical density was measured at all eight sites, it was only significant at the load-bearing Tibia (4% site) across neuromuscular disorders, chronic diseases and endocrine diseases, and only observed in the Fibula (4% site) for neuromuscular disorders. The variables of muscle area (two sites), mid-cortical ring density (three sites), endocortical radius (three sites), and midcortical density (one site) were not significant for any of the bone sites measured. Additional muscle and bone parameters were analysed for this study with full descriptions provided in Supplementary Tables 1 to 8.   Table 3. General Linear Model (GLM) differences in lower limb regional bone parameters to the control group (p-values). All significant values bolded (*) if significantly lower than control, or (^) if significantly higher than control. n = sample sizes.

Discussion
This cross-sectional observational study compared pQCT data from regional bone analyses of youth at risk of osteoporosis secondary to specific chronic diseases, youth with low motor competence, and non-affected controls. Our primary finding were that changes in regional appendicular long bone parameters were predominantly seen in youth with neuromuscular disorders, endocrine disorders and low motor competence. Our secondary findings were that the lower extremity appeared to be more susceptible to these disease-specific changes: the distal lower leg sites (4% of tibia and fibula) were the only sites significantly lower to the control group for all measured variables. In line with the present understanding of the explored clinical conditions causing secondary osteoporosis, no differences between groups were observed in diaphyseal CoD, a characteristic, which would indicate abnormal mineralisation. As shown in adults with osteoporosis 9 , cortical and trabecular parameters were affected in youth at risk of secondary osteoporosis. Cortical area (a key structural variable that confers strength to long bones 33 and is responsive to physical activity interventions 34 ) was significantly different for all eight bone sites for all disease groups and the low motor competence group when compared to the non-affected control group. Trabecular density was affected in tibia for neuromuscular disorders, endocrine diseases and chronic disease, and the fibula for neuromuscular and chronic disease. While tibial muscle density was suboptimal for neuromuscular disorders and chronic diseases, suggesting a link between decreased opportunities for bone loading through physical activity.
In adults, fractures are considered an indicator of osteoporosis resulting from changes in bone structure and matrix 9,35 Fiscaletti and colleagues 36 assert that recurrent fractures in youth warrant further investigations for primary genetic forms of osteoporosis such as osteogenesis imperfecta. However, there is also likely a proportion of individuals presenting with recurrent fractures are likely affected by secondary osteoporosis. In this group, life style factors gain importance as contributing to an increased risk of fractures. There are no known specific fracture rates reported for endocrine diseases, inborn errors of metabolism, or for any iatrogenic effects, however all groups do have a higher fracture incidence than nonaffected children 37-39 . The fracture incidence for youth on glucocorticoid therapy is 1.32 times greater compared to a non-affected population 37 . Higher fracture incidence rates have been reported for children with neuromuscular disorders, low motor competences and chronic diseases. For muscle disorders, these mainly affect the lower limbs 40 . Sheung-Tung 41 showed that children with cerebral palsy (a neuromuscular disorder) have a fracture incidence of 480 per 10,000 people. Individuals with low motor competencies have a higher prevalence of fractures compared to nonaffected individuals 7,42 . It can be hypothesized that these groups presented with compromised bone health associated with early structural changes with reduced mechanical loading as one potential causative factor 6,7,40-42 . In support, our GLM analysis revealed that the neuromuscular disorders and low motor competence groups showed the most significant differences in structural bone parameters across upper limb pQCT measures when compared to the nonaffected control group (Table 2). This aligns with the known fine and gross motor difficulties individuals with DCD and low motor competence experience performing activities such as writing, throwing and catching, decreased participation in such activities during their childhood, and low muscle force 43 . Further, structural differences in lower limb pQCT measures for neuromuscular, chronic and endocrine diseases, and low motor competence, including for some muscle parameters, also points at the importance of physical activity as a lifestyle factor contributing to bone health.
Reflecting on our findings, pathology in neuromuscular disorders is influenced by the fact that in addition to limitations of lower limb adaptation, rates of upper body movement are also reduced, thus accounting for the corresponding negative changes in upper limb long bone characteristics 40 . Similarly for children and adolescents with low motor competence physical activity is compromised by their difficulties in the acquisition and execution of basic movement skills such as running, jumping and activities of daily living compared to non-affected populations 44 . A potential explanation for changes in structural bone and muscle parameters in chronic disease ( Table 2 and Table 3) is their decreased movement compared to non-affected populations 45-47 . This results from a reduction in the efficacy of voluntary movements (activities of daily living) due to disabilities preventing movement such as a stroke and arthritis, and decreased endurance capacity due to conditions such as chronic non-specific lung disease and cardiac disease 45-47 . This is reflected by significantly lower values for the chronic diseases group in comparison to healthy controls at lower limb bone sites for CoD, CoA, SSI, BSI, and trabecular density. Endocrine diseases only reported significant differences for the Tibia 4%, suggesting the sample size was adequate to detect differences if they existed, which may be an early systemic indication of suboptimal endocrine effects on bone metabolism at the distal trabecular bone site 48,49 . Conversely, iatrogenic conditions and inborn errors of metabolism groups had no significantly different variables to the control group. While it could be speculated that (a) their specific pathology does not affect neuromuscular capacity and (b) although iatrogenic causes can result in low motor competencies, individuals may have only received their medication for a short period of time thus have not experienced the lifetime negative effects of compromised motor development as seen with individuals with low motor competence 50 , the very low samples size for both these groups indicates the analysis was underpowered and no conclusions can be drawn.
The results from this study, particularly sample sizes across disease profiles, highlight the clinical difficulty and novel utility of observing some disease groups. The results are expected to inform future work and provide data for future power calculations. For this study in particular there were a small number of individuals from inborn errors of metabolism (n=5) and iatrogenic conditions (n=12), and therefore it is likely these groups were underpowered for statistical analysis. Future work should look at pooling national and international data to obtain larger sample sizes. It should also be noted that as a cross-sectional observational study, the inclusion and exclusion criteria was not clearly defined and it would be beneficial to examine these findings prospectively in light of other confounding factors, particularly physical activity levels and information on mechanical loading. Furthermore, the slight difference in voxel size between two of the data-sets (0.4 mm AMPitup/PMH vs. 0.5 mm Griffith) should be noted. Motion artefact is a plausible limitation, however as reported in our methodology, we excluded scans with artefact rated four or five 29 to minimise this influence. While growth, and in particular age, are acknowledged as potential factors for these between group comparisons, this was statistically accounted for in the modelling procedures undertaken by including age and age squared 31 in the model, and therefore group differences detected most likely reflect a true effect independent of age or growth. Another limitation of this study is lack of information on pubertal status, which is known to play a role in bone adaptation. With respect to this, the GLM included bone length as a proxy factor for puberty.
In summary, pQCT analyses of appendicular regional bone parameters in youth at risk of secondary osteoporosis revealed changes in structural bone and muscle parameters. These were particularly pronounced in individuals with neuromuscular disorders, chronic diseases and low motor competence suggesting an impact of changes in mechanical loading resulting from reduced capacity of physical activity and movement. We conclude that detailed characterisation of peripheral bone health has the potential to identify areas for targeted exercise interventions to optimise bone health and peak bone mass accrual in youth.