Cross-sectional reference data for phalangeal quantitative ultrasound from early childhood to young-adulthood according to gender, age, skeletal growth, and pubertal development
Introduction
The assessment of bone mineral status in children and adolescents may be a useful tool in identifying the subjects who could be exposed to an increased risk of osteoporosis in adulthood. Some techniques, such as dual energy X-ray absorptiometry (DXA) and peripheral quantitative computed tomography (pQCT), may be used to this purpose, but the exposure to ionizing radiation is a limiting factor for preventive studies. Quantitative ultrasound (QUS) methods have been developed to assess bone mineral status in some skeletal sites including calcaneus, phalanges of the hand, and tibia. These techniques are safe, easy to use, and radiation-free, and devices are portable [1], [2], so that they are particularly indicated to assess bone mineral status in children and adolescents.
Clinical studies showed that phalangeal QUS is able to discriminate between osteoporotic and healthy women [1], [3], [4], [5], [6], and it may predict fracture independent of bone mineral density (BMD) [3], [7], [8], [9]. Moreover, it has been shown that phalangeal QUS is a useful method to assess bone mineral status and fracture risk in children and adolescents with bone and mineral disorders [10], as well as in healthy children [11].
Some studies reported changes or normative data for some phalangeal QUS parameters, such as amplitude-dependent speed of sound (AD-SoS) or bone transmission time (BTT) [12], [13], [14], [15], [16], [17], [18], [19] in children and/or adolescents of various ages, but a normative database according to the main anthropometric findings for both these QUS parameters from early childhood to young-adulthood is lacking.
The aim of the study was to assess phalangeal QUS in a large sample of healthy infants, children, and adolescents in order to provide a valuable reference database for AD-SoS and BTT, both expressed as centiles and Z score, according to gender, chronological age, height, weight, body mass index (BMI), and pubertal stage to be used for estimating bone mineral status in patients with growth or pubertal disturbances, and in patients affected by bone and mineral disorders.
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Subjects
A total of 3044 (1513 males and 1531 females) healthy white subjects, aged 2–21 years, randomly recruited in nursery, primary, or secondary schools, and in medical schools of some cities located in northern (Udine: n = 301, 146 males and 155 females; Pavia: n = 181, 101 males and 80 females; Genoa: n = 839, 430 males and 409 females), central (Pisa: n = 1097, 593 males and 504 females; L'Aquila: n = 105 females) or southern (Naples: n = 412, 208 males and 204 females, Catanzaro: n = 109, 35
Static distance curves for AD-SoS and BTT according to gender and age
Static distance curves according to gender and age for AD-SoS and BTT, expressed as centiles, are reported in Fig. 1, Fig. 2, respectively. There was no difference (P = NS) in QUS data among the different centres.
Mean AD-SoS values increased with age from 1789.3 m/s and 1802.0 m/s at age 2 to 2123.4 m/s and 2101.2 m/s at age 21.9 in males and females, respectively (Fig. 3); the increment between 2 and 21.9 years was 18.7% (P < 0.0001) and 16.6% (P < 0.0001) in males and females, respectively.
Discussion
Ultrasound velocity reflects the material properties of bone and is influenced by bone density, architecture, and elasticity [3], [21], [27]. Studies in proximal phalanx of the forelimb of healthy young pigs [28] and in human cadaveric lumbar vertebrae [29] showed that ultrasound velocity was related more strictly to bone density than to bone elasticity. In children and adolescents, it was demonstrated that the changes of AD-SoS and BTT reflected the architectural organization of growing bone
Acknowledgments
The authors thank the headmasters, school teachers, and parents who gave their consent to the study and helped us in its organization. Moreover, we are very grateful to Professor TJ Cole, Department of Epidemiology and Public Health, Institute of Child Health, London, United Kingdom, who provided us the dedicated LMS software to calculate LMS data for QUS parameters.
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Arianna Parodi (Paediatric Clinic, University of Genoa, Institute “G. Gaslini”, Genoa, Italy), Antonella Esposito (Rheumatology Unit, University “Federico II”, Naples, Italy), Natascia di Iorgi (Department of Pediatrics, University of Genoa, Institute “G. Gaslini”, Genoa, Italy), Tiziana Muratori (Department of Pediatrics, IRCCS Policlinico San Matteo, University of Pavia, Italy), Immacolata Guzzo (Department of Pediatrics, University “Magna Graecia”, Catanzaro, Italy), Anna Rita Frascogna (Department of Pediatrics, University “Magna Graecia”, Catanzaro, Italy), Katia Pellegrino (Pediatric Clinic, L'Aquila, Italy), Loredana Mastidoro (Department of Orthopaedics and Traumatology, “Ospedale S. Maria della Misericordia”, Udine, Italy), Aldo Filosa (UOC Pediatria, AORN “A. Cardarelli”, Naples, Italy), Francesca de Terlizzi (IGEA Biophysics Laboratory, Carpi, Modena, Italy), Stefano Battista (IGEA Biophysics Laboratory, Carpi, Modena, Italy), Ruggero Cadossi (IGEA Biophysics Laboratory, Carpi, Modena, Italy).