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Pediatric dual-energy X-ray absorptiometry: interpretation and clinical and research application

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DOI : 10.3345/kjp.2010.53.3.286 Korean Journal of Pediatrics Vol. 53, No. 3, 2010 Review article 1)jtj Pediatric dual-energy X-ray absorptiometry: interpretation and clinical and research application Jung Sub Lim, M.D. Department of Pediatrics, Korean Cancer Center Hospital = Abstract = Peak bone mass is established predominately during childhood and adolescence. It is an important determinant of future resistance to osteoporosis and fractures to gain bone mass during growth. The issue of low bone density in children and adolescents has recently attracted much attention and the use of pediatric dual-energy X-ray absorptiometry (DXA) is increasing. The process of interpretation of pediatric DXA results is different from that of adults because normal bone mineral density (BMD) of children varies by age, body size, pubertal stage, skeletal maturation, sex, and ethnicity. Thus, an appropriate normal BMD Z-score reference value with Z-score should be used to detect and manage low BMD. Z-scores below -2.0 are generally considered a low BMD to pediatrician even though diagnoses of osteoporosis in children and adolescents are usually only made in the presence of at least one fragility fracture. This article will review the basic knowledge and practical guidelines on pediatric DXA based on the International Society for Clinical Densitometry (ISCD) Pediatric Official Positions. Also discussed are the characteristics of normal Korean children and adolescents with respect to BMD development. The objective of this review is to help pediatricians to understand when DXA will be useful and how to interpret pediatric DXA reports in the clinical practice for management of children with the potential to develop osteoporosis in adulthood. (Korean J Pediatr 2010;53:286-293) Key Words : Dual-energy X-ray absorptiometry (DXA), Osteoporosis, Low bone mineral density, Fracture, Children and Adolescence Introduction Osteoporosis is a major global public health concern. The incidence of osteoporosis and its subsequent morbidity is expected to increase dramatically over the coming decades in many regions, including Korea. Although it is considered as a disease of the elderly, there is now universal agreement that osteoporosis has a pediatric origin1). If individuals fail to achieve optimal peak bone mass (PBM) and strength childhood and adolescence, there is a likelihood of development osteoporosis later in life2). Genetic factors play an important role in the attainment of PBM. Lifestyle factors such as physical activity and nutrition are also important3). Chronic illness itself and various related treatments also tend to cause impairment of acquisition of bone mass, Received : 14 January 2010, Accepted : 18 February 2010 Address for correspondence : Jung Sub Lim, M.D. Department of Pediatrics, Korean Cancer Center Hospital, 215 Gongneung-dong, Nowon-gu, Seoul 139-706, Korea Tel : +82.2- 970-1224, Fax : +82.2-970-2403 E-mail : limjs@kcch.re.kr long-term adult bone health and to increase the risk of fracture4, 5). Furthermore, increased knowledge and improved care for children with chronic illness has led to many children living long enough to develop osteoporosis even in childhood or adolescence4, 5). Measurement of bone mineral density (BMD) by dualenergy X-ray absorptiometry (DXA) is the gold standard method for non-invasive diagnosis of osteoporosis6, 7). The World Health Organization (WHO) reported a classification of BMD for the diagnosis of osteoporosis based on DXA. DXA is easy to perform, safe, and clinically acceptable for children as well as adults. Thus, the use of pediatric DXA in the clinical and research fields has rapidly increased8). This article will discuss the basics of pediatric DXA and will review normal stages of BMD development of each region of interest (ROI) and PBM acquisition in Korean children and adolescents. The indications for pediatric DXA, clinical practice of pediatric DXA including cautions on interpretation, and other research applications of DXA will be described. - 286 - Characteristics of bone mass acquisition in Korean children and adolescence The most important aspect of pediatric DXA is to use an appropriate normative data set because the BMD and PBM differences are dependent on age, puberty, sex, and ethnicity6, 9). A BMD normative data set for Korean children and adolescents has been recently established and published10, 11). One notable characteristic of Korean children and adolescents is an earlier onset of BMD acquisition. Otherwise, the BMD and PBM trends are similar to those of other ethnicities. BMD accretion at the lumbar spine in Korean girls has the highest rate between ages 11 to 13, while boys have the highest rate between ages 12 to 14, about six months after peak height velocity. Dutch children have the highest rate of lumbar spine BMD accretion that occurs one year later than in Korean children10). When compared with Korean adults, the lumbar spine BMD and femur neck BMD values of Korean girls over 18 yr and Korean boys over 19 yr were the same as the values measured for 20 to 30-yr-old Koreans12). Therefore, Koreans achieve essentially the same peak BMD in the lumbar spine and femur neck late in the second decade of life. On average, 90% of peak bone mass (PBM) is acquired by the age of 19 in other ethnicities2, 7). During puberty, Koreans have an increase in lumbar spine BMD, which is similar to that of other ethnic groups. The percentages of BMD acquisition at the lumbar spine in Koreans between Tanner stage (TS) 1 and TS 5 were found to be 65% in girls and 66% in boys. Koreans also tend to have a higher rate of trabecular bone mass (lumbar spine) acquisition than cortical bone mass acquisition (total body or femur neck) during puberty. The whole body BMD increases to 43% in girls and 51% in boys. Korean girls also have an earlier onset of the BMD plateau than boys, as observed for other ethnicities. The plateaus of the lumbar spine BMD and whole body BMD in girls occurred at ages 15 and 17 respectively. The plateaus of BMDs in each ROI occurred at age 17 in boys10). Bone mineral density and size in the prediction of fracture risk Diagnosis of osteoporosis in children remains challen- ging. In adults, a DXA T-score is defined as the number of standard deviations (SDs) away from the mean BMD of a healthy young population. Several epidemiological studies have confirmed the association between a low BMD Tscore and fracture risk in the elderly population. The fracture risk doubles for every SD decrease in BMD T-score 13). Thus, DXA became the principal tool for diagnosing adult osteoporosis. In children, the association between low BMD Z-score and fracture risk is not well established. However, ISCD adopted DXA in assessing bone mass in children and growing evidence suggested that low bone mass might contribute to fracture risk in childhood14, 15). Fractures are common and the prime reason for hospitalization of children. Forty-two percent of boys and 27% of girls experience at least one fracture between the age of 0 to 1616). Studies in generally healthy children have found that those who sustain a forearm fracture have a lower mean bone density than peers without a history of fracture17). Recently, a systematic review and meta-analysis of the association between bone density and fractures in otherwise healthy children concluded that lower total body and spine BMD can be a predictor of an upper extremity fracture during puberty14). The total body less head (TBLH; the ROI of total body after subtraction of cranium) bone mineral content (BMC) adjusted bone area is expected to increase the risk of fracture by about 89% per each SD decrease. In the same cohort, it was found that fracture risk from both slight and moderate severe trauma is related to changes in TBLH bone size relative to body size18). A small increase in the bone diameter will increase bone strength markedly19). Indications for pediatric DXA Before ordering a DXA analysis, pediatricians should consider how the information will influence clinical management6, 20). In adults, DXA is performed to predict fracture, to decide which patients warrant treatment, and to monitor response to therapy. The rationale for pediatric DXA is potentially the same in children. The International Society for Clinical Densitometry (ISCD) has suggested that the DXA analysis be carried out for any child who is being treated or considered for treatment of osteoporosis5). Children whose potential fracture risk is likely to exceed that of normal children should obtain DXA measurements. This will include children with primary bone diseases (such as - 287 - osteogenesis imperfecta and idiopathic juvenile osteoporosis). Children with secondary conditions that affect bone health should also obtain DXA measurements (Such secondary conditions include immobilization, inflammation, endocrine disturbance, malignancy and treatment, transplantation recovery apparent osteopenia on radiographs, and systemic use of long-term steroids)4, 5, 21). Previous studies have established that children with chronic disease have lower BMD than their healthy counterparts4, 5). Certain children treated with specific medications, such as corticosteroids, anticonvulsants, and chemotherapeutic drugs, do not acquire adequate BMD during growth, and, thus, have an increased risk of fractures in later life21, 22). In addition, most children with chronic disease are subject to risks in skeletal health as a result of a combination of risk factors including malnutrition, malabsorption, vitamin D insufficiency, immobilization, deficiency or resistance to sex steroids or growth hormone, and increased cytokine production5). Pediatric DXA testing is not routinely indicated for the evaluation of all chronic disease. Any additional risk factors such as disease severity, dose and duration of exposure to potentially harmful medication, bone pain, a history of fracture after minimal trauma, osteopenia on a plain film, and recurrent or low-impact fractures history are useful parameters for identification of candidates for DXA testing. Causes of pediatric osteoporosis and commonly indicated diseases for DXA are listed in Table 1. Ordering a pediatric DXA analysis The posterior-anterior (PA) lumbar spine and TBLH are recommended sites for performing BMC and areal BMD measurements in both children and adolescents because the most accurate and reproducible measurements can be obtained in these areas. The hip (including proximal femur and total hip) is not a reliable site for measurement in growing children due to significant variability in skeletal development, lack of reproducibility and limited normal reference data6). However, selection of regions of interest (ROI) for DXA analysis depends upon clinical concerns and the options within the clinical setting8). For example, sex steroid deficiency typically causes greater loss in trabecular bone23). Selecting lumbar spine as the ROI to be scanned is appropriate as spine is rich in trabecular bone. On the other hand, growth hormone deficiency causes a predispositions to greater loss of cortical 1. ' ' ' - () ( (21 ) ) ' ( ) () / () - 288 - , bone. In that case, total body scans may be needed24). In the evaluation of localized osteoporosis in osteosarcoma patients, the hip scan is advised in case of appropriate pediatric reference data are available. In osteosarcoma patients, comparisons of both extremities and follow-up studies of the affected limbs are needed. There is a significant association between TBLH and fractures. As a result, the ISCD recommends TBLH measurements instead of total body measurements. However, many studies show that the total body BMD scan is used clinically because most pediatric-based software can only provide total body results. The pediatric normal references of TBLH are limited. Analysis of Pediatric DXA Results In a pediatric DXA report, the Z-score should be assessed instead of the T-score. Subsequently, the patient s anthropometric data, including age, gender, ethnicity, weight, height, and Tanner stage should be verified. Next, the patient s position and analyzed ROIs should be verified as appropriate, and it should be confirmed that no artifacts exist, which would lead to abnormal results. The proper patient position for DXA scanning is illustrated in Fig 1. The lumbar spine should be straightened and centered in the image with visualization of the last rib pair and the upper sacrum. The femur neck and the femoral shaft should be parallel to the long axis of the image with only a small portion of the lesser trochanter visualized. Total body scanning after proper positioning according to the machine type provides measurements of total body BMC, BMD, and body composition including fat, lean body mass, bone mineral content, and percentage of fat. Extraneous artifacts, including buttons, coins, enteric tubes, and orthopedic hardware should be excluded from the image. The next step is to check the Z-score. The Z-score is a standard deviation score compared to a Korean normal control adjusted for age and sex. A proper control should be obtained for interpretation of pediatric DXA results. A common mistake of erroneous interpretation of pediatric DXA is to use a T-score based upon a comparison with peak adult BMD. In one report, 62% of children for referred for osteoporosis were misdiagnosed because adult reference data was used rather than pediatric norms25). The T-score measures the bone density loss occurring from early adulthood. Its use for analyzing pediatric data will cause a significant misdiagnosis6). When T-scores are obtained, adult software is used to analyze BMD instead of pediatric software. The analysis algorithm of the adult software significantly overestimates the lumbar BMD and underestimates the lumbar BMC relative to the pediatric software because pediatric bone is naturally less dense than adult bone26). The algorithm used in the pediatric DXA software is adapted for improved edge detection of lower density pediatric bone in order to address this problem7, 20). The final step is to verify that the correct version of the DXA software was used and include this verification in the DXA reports. Interpretation of Pediatric DXA In children and adolescents, the terms low bone mineral content or low bone mineral density for chronological age have been recommended for use in DXA reports rather than the terms osteopenia and osteoporosis6). Importantly, the diagnosis of osteoporosis should not be made solely on the basis of DXA results . Diagnosis of osteoporosis requires a clinically significant fracture history such as one longbone fracture of the lower extremity, vertebral compression fracture, and two or more long-bone fractures of the upper extremities in addition to low bone mass. Low bone mass is diagnosed when a BMC or areal BMD Z-score of known ROI is less than or equal to -2.0 (with the Z-score adjusted for age, sex, and body size). If Z scores are not provided by the DXA software, published pediatric reference data can be used to calculate them. Several DXA studies providing normative data from healthy Korean children are summarized in Table 2. It is essential to use a normal reference obtained from the same instrument because there are systematic differences among the different DXA machines. The pattern of mineral accrual is linked more closely to pubertal and skeletal maturation than to chronologic age, and these processes tend to vary with gender and ethnicity 6, 27). For example, children with precocious puberty have abnormally increased BMD relative to chronological age. On the other hand, children with constitutional delay exhibit decreased BMD. For this reason, the influence of height, bone size, and maturation must be considered during evaluation of DXA results. Korean children and adolescents often have a discrepancy between chronological age and bone age10). In particular, many children and adolescents - 289 - 2. 200910) 200711) 200933) 199834) 199535) ( .) . . . . . . 26 2000 514 5-20 446 2-18 135 6-14 75 2-15 53 4-13 ,, ,, , , , with chronic disease have a delayed growth, absent or arrested puberty, and altered body composition. The DXA-derived BMD is based upon two dimensional projection areas of three dimensional structures and provides an areal BMD rather than volumetric (true) BMD. This can causes several problems5, 6), the most significant of which is that areal bone density may be underestimated in children with smaller bones and overestimated in larger children5, 6). Thus several methods have been proposed to solve this problem. One of the commonly used methods makes use of the apparent BMD (BMAD) , a mathematically calculated volumetric BMD. The calculation for BMAD is: BMAD (g/cm3)=BMDLS [4/(π width)]28). Another measure is the adjusted bone mass that is obtained by correcting the lean body mass or height to minimize the influence of bone size or lean body mass29, 30). Another possible way of making corrections to the BMD is to use bone age. Height and skeletal maturation generally correlate with bone age rather than chronological age. Bone age is more accurate than chronological age in assessing each individual BMD6, 10). However, none of the abovementioned correction methods has been established as the best method according to the gold standard of successful prediction of childhood fracture. Nonetheless, it is possible to estimate how much a reduced BMD can be attributed to smaller bone size by calculating volumetric BMD or using other corrective methods. Clinical case studies The following common examples are pediatrics DXA studies interpreted at the Korea Cancer Center Hospital. The reference BMD employed for each ROI is obtained from Bone Mineral Density according to Age, Bone Age, equilibrium value of MeCpG steps (,+14 deg.) [31,44]. In comparison, methylation has a significantly lower stability cost when happening at major groove positions, such as 211 and 21 base pair from dyad (mutations 9 and 12), where the roll of the nucleosome bound conformation (+10 deg.) is more compatible with the equilibrium geometry of MeCpG steps. The nucleosome destabilizing effect of cytosine methylation increases with the number of methylated cytosines, following the same position dependence as the single methylations. The multiple-methylation case reveals that each major groove meth- PLOS Computational Biology | www.ploscompbiol.org 3 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning ylation destabilizes the nucleosome by around 1 kJ/mol (close to the average estimate of 2 kJ/mol obtained for from individual methylation studies), while each minor groove methylation destabilizes it by up to 5 kJ/mol (average free energy as single mutation is around 6 kJ/mol). This energetic position-dependence is the reverse of what was observed in a recent FRET/SAXS study [30]. The differences can be attributed to the use of different ionic conditions and different sequences: a modified Widom-601 sequence of 157 bp, which already contains multiple CpG steps in mixed orientations, and which could assume different positioning due to the introduction of new CpG steps and by effect of the methylation. The analysis of our trajectories reveals a larger root mean square deviation (RMSD) and fluctuation (RMSF; see Figures S2– S3 in Text S1) for the methylated nucleosomes, but failed to detect any systematic change in DNA geometry or in intermolecular DNA-histone energy related to methylation (Fig. S1B, S1C, S4–S6 in Text S1). The hydrophobic effect should favor orientation of the methyl group out from the solvent but this effect alone is not likely to justify the positional dependent stability changes in Figure 2, as the differential solvation of the methyl groups in the bound and unbound states is only in the order of a fraction of a water molecule (Figure S5 in Text S1). We find however, a reasonable correlation between methylation-induced changes in hydrogen bond and stacking interactions of the bases and the change in nucleosome stability (see Figure S6 in Text S1). This finding suggests that methylation-induced nucleosome destabilization is related to the poorer ability of methylated DNA to fit into the required conformation for DNA in a nucleosome. Changes in the elastic deformation energy between methylated and un-methylated DNA correlate with nucleosomal differential binding free energies To further analyze the idea that methylation-induced nucleosome destabilization is connected to a worse fit of methylated DNA into the required nucleosome-bound conformation, we computed the elastic energy of the nucleosomal DNA using a harmonic deformation method [36,37,44]. This method provides a rough estimate of the energy required to deform a DNA fiber to adopt the super helical conformation in the nucleosome (full details in Suppl. Information Text S1). As shown in Figure 2, there is an evident correlation between the increase that methylation produces in the elastic deformation energy (DDE def.) and the free energy variation (DDG bind.) computed from MD/TI calculations. Clearly, methylation increases the stiffness of the CpG step [31], raising the energy cost required to wrap DNA around the histone octamers. This extra energy cost will be smaller in regions of high positive roll (naked DNA MeCpG steps have a higher roll than CpG steps [31]) than in regions of high negative roll. Thus, simple elastic considerations explain why methylation is better tolerated when the DNA faces the histones through the major groove (where positive roll is required) that when it faces histones through the minor groove (where negative roll is required). Nucleosome methylation can give rise to nucleosome repositioning We have established that methylation affects the wrapping of DNA in nucleosomes, but how does this translate into chromatin structure? As noted above, accumulation of minor groove methylations strongly destabilizes the nucleosome, and could trigger nucleosome unfolding, or notable changes in positioning or phasing of DNA around the histone core. While accumulation of methylations might be well tolerated if placed in favorable positions, accumulation in unfavorable positions would destabilize the nucleosome, which might trigger changes in chromatin structure. Chromatin could in fact react in two different ways in response to significant levels of methylation in unfavorable positions: i) the DNA could either detach from the histone core, leading to nucleosome eviction or nucleosome repositioning, or ii) the DNA could rotate around the histone core, changing its phase to place MeCpG steps in favorable positions. Both effects are anticipated to alter DNA accessibility and impact gene expression regulation. The sub-microsecond time scale of our MD trajectories of methylated DNAs bound to nucleosomes is not large enough to capture these effects, but clear trends are visible in cases of multiple mutations occurring in unfavorable positions, where unmethylated and methylated DNA sequences are out of phase by around 28 degrees (Figure S7 in Text S1). Due to this repositioning, large or small, DNA could move and the nucleosome structure could assume a more compact and distorted conformation, as detected by Lee and Lee [29], or a slightly open conformation as found in Jimenez-Useche et al. [30]. Using the harmonic deformation method, we additionally predicted the change in stability induced by cytosine methylation for millions of different nucleosomal DNA sequences. Consistently with our calculations, we used two extreme scenarios to prepare our DNA sequences (see Fig. 3): i) all positions where the minor grooves contact the histone core are occupied by CpG steps, and ii) all positions where the major grooves contact the histone core are occupied by CpG steps. We then computed the elastic energy required to wrap the DNA around the histone proteins in unmethylated and methylated states, and, as expected, observed that methylation disfavors DNA wrapping (Figure 3A). We have rescaled the elastic energy differences with a factor of 0.23 to match the DDG prediction in figure 2B. In agreement with the rest of our results, our analysis confirms that the effect of methylation is position-dependent. In fact, the overall difference between the two extreme methylation scenarios (all-in-minor vs all-in-major) is larger than 60 kJ/mol, the average difference being around 15 kJ/ mol. We have also computed the elastic energy differences for a million sequences with CpG/MeCpG steps positioned at all possible intermediate locations with respect to the position (figure 3B). The large differences between the extreme cases can induce rotations of DNA around the histone core, shifting its phase to allow the placement of the methylated CpG steps facing the histones through the major groove. It is illustrative to compare the magnitude of CpG methylation penalty with sequence dependent differences. Since there are roughly 1.5e88 possible 147 base pairs long sequence combinations (i.e., (4n+4(n/2))/2, n = 147), it is unfeasible to calculate all the possible sequence effects. However, using our elastic model we can provide a range of values based on a reasonably large number of samples. If we consider all possible nucleosomal sequences in the yeast genome (around 12 Mbp), the energy difference between the best and the worst sequence that could form a nucleosome is 0.7 kj/mol per base (a minimum of 1 kJ/mol and maximum of around 1.7 kJ/mol per base, the first best and the last worst sequences are displayed in Table S3 in Text S1). We repeated the same calculation for one million random sequences and we obtained equivalent results. Placing one CpG step every helical turn gives an average energetic difference between minor groove and major groove methylation of 15 kJ/ mol, which translates into ,0.5 kJ/mol per methyl group, 2 kJ/ mol per base for the largest effects. Considering that not all nucleosome base pair steps are likely to be CpG steps, we can conclude that the balance between the destabilization due to CpG methylation and sequence repositioning will depend on the PLOS Computational Biology | www.ploscompbiol.org 4 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning Figure 3. Methylated and non-methylated DNA elastic deformation energies. (A) Distribution of deformation energies for 147 bplong random DNA sequences with CpG steps positioned every 10 base steps (one helical turn) in minor (red and dark red) and major (light and dark blue) grooves respectively. The energy values were rescaled by the slope of a best-fit straight line of figure 2, which is 0.23, to source of circulating FGF-21. The lack of association between circulating and muscle-expressed FGF-21 also suggests that muscle FGF-21 primarily works in a local manner regulating glucose metabolism in the muscle and/or signals to the adipose tissue in close contact to the muscle. Our study has some limitations. The number of subjects is small and some correlations could have been significant with greater statistical power. Another aspect is that protein levels of FGF-21 were not determined in the muscles extracts, consequently we cannot be sure the increase in FGF-21 mRNA is followed by increased protein expression. In conclusion, we show that FGF-21 mRNA is increased in skeletal muscle in HIV patients and that FGF-21 mRNA in muscle correlates to whole-body (primarily reflecting muscle) insulin resistance. These findings add to the evidence that FGF-21 is a myokine and that muscle FGF-21 might primarily work in an autocrine manner. Acknowledgments We thank the subjects for their participation in this study. Ruth Rousing, Hanne Willumsen, Carsten Nielsen and Flemming Jessen are thanked for excellent technical help. The Danish HIV-Cohort is thanked for providing us HIV-related data. PLOS ONE | www.plosone.org 6 March 2013 | Volume 8 | Issue 3 | e55632 Muscle FGF-21,Insulin Resistance and Lipodystrophy Author Contributions Conceived and designed the experiments: BL BKP JG. Performed the experiments: BL TH TG CF PH. Analyzed the data: BL CF PH. Contributed reagents/materials/analysis tools: BL. Wrote the paper: BL. References 1. Kharitonenkov A, Shiyanova TL, Koester A, Ford AM, Micanovic R, et al. (2005) FGF-21 as a novel metabolic regulator. J Clin Invest 115: 1627–1635. 2. Coskun T, Bina HA, Schneider MA, Dunbar JD, Hu CC, et al. (2008) Fibroblast growth factor 21 corrects obesity in mice. Endocrinology 149: 6018– 6027. 3. Xu J, Lloyd DJ, Hale C, Stanislaus S, Chen M, et al. (2009) Fibroblast growth factor 21 reverses hepatic steatosis, increases energy expenditure, and improves insulin sensitivity in diet-induced obese mice. Diabetes 58: 250–259. 4. Inagaki T, Dutchak P, Zhao G, Ding X, Gautron L, et al. (2007) Endocrine regulation of the fasting response by PPARalpha-mediated induction of fibroblast growth factor 21. Cell Metab 5: 415–425. 5. Potthoff MJ, Inagaki T, Satapati S, Ding X, He T, et al. (2009) FGF21 induces PGC-1alpha and regulates carbohydrate and fatty acid metabolism during the adaptive starvation response. Proc Natl Acad Sci U S A 106: 10853–10858. 6. Badman MK, Pissios P, Kennedy AR, Koukos G, Flier JS, et al. (2007) Hepatic fibroblast growth factor 21 is regulated by PPARalpha and is a key mediator of hepatic lipid metabolism in ketotic states. Cell Metab 5: 426–437. 7. Zhang X, Yeung DC, Karpisek M, Stejskal D, Zhou ZG, et al. (2008) Serum FGF21 levels are increased in obesity and are independently associated with the metabolic syndrome in humans. Diabetes 57: 1246–1253. 8. Chen WW, Li L, Yang GY, Li K, Qi XY, et al. (2008) Circulating FGF-21 levels in normal subjects and in newly diagnose patients with Type 2 diabetes mellitus. Exp Clin Endocrinol Diabetes 116: 65–68. 9. Chavez AO, Molina-Carrion M, Abdul-Ghani MA, Folli F, DeFronzo RA, et al. (2009) Circulating fibroblast growth factor-21 is elevated in impaired glucose tolerance and type 2 diabetes and correlates with muscle and hepatic insulin resistance. Diabetes Care 32: 1542–1546. 10. Hojman P, Pedersen M, Nielsen AR, Krogh-Madsen R, Yfanti C, et al. (2009) Fibroblast growth factor-21 is induced in human skeletal muscles by hyperinsulinemia. Diabetes 58: 2797–2801. 11. Izumiya Y, Bina HA, Ouchi N, Akasaki Y, Kharitonenkov A, et al. (2008) FGF21 is an Akt-regulated myokine. FEBS Lett 582: 3805–3810. 12. Vienberg SG, Brons C, Nilsson E, Astrup A, Vaag A, et al. (2012) Impact of short-term high-fat feeding and insulin-stimulated FGF21 levels in subjects with low birth weight and controls. Eur J Endocrinol 167: 49–57. 13. Carr A, Samaras K, Burton S, Law M, Freund J, et al. (1998) A syndrome of peripheral lipodystrophy, hyperlipidaemia and insulin resistance in patients receiving HIV protease inhibitors. AIDS 12: F51–F58. 14. Haugaard SB, Andersen O, Dela F, Holst JJ, Storgaard H et al. (2005) Defective glucose and lipid metabolism in human immunodeficiency virus-infected patients with lipodystrophy involve liver, muscle tissue and pancreatic betacells. Eur J Endocrinol 152: 103–112. 15. Reeds DN, Yarasheski KE, Fontana L, Cade WT, Laciny E, et al. (2006) Alterations in liver, muscle, and adipose tissue insulin sensitivity in men with HIV infection and dyslipidemia. Am J Physiol Endocrinol Metab 290: E47–E53. 16. Meininger G, Hadigan C, Laposata M, Brown J, Rabe J, et al. (2002) Elevated concentrations of free fatty acids are associated with increased insulin response to standard glucose challenge in human immunodeficiency virus-infected subjects with fat redistribution. Metabolism 51: 260–266. 17. Carr A, Emery S, Law M, Puls R, Lundgren JD, et al. (2003) An objective case definition of lipodystrophy in HIV-infected adults: a case-control study. Lancet 361: 726–735. 18. Lindegaard B, Hansen T, Hvid T, van HG, Plomgaard P, et al. (2008) The effect of strength and endurance training on insulin sensitivity and fat distribution in human immunodeficiency virus-infected patients with lipodystrophy. J Clin Endocrinol Metab 93: 3860–3869. 19. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, et al. (1985) Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28: 412– 419. 20. Lindegaard B, Frosig C, Petersen AM, Plomgaard P, Ditlevsen S, et al. (2007) Inhibition of lipolysis stimulates peripheral glucose uptake but has no effect on endogenous glucose production in HIV lipodystrophy. Diabetes 56: 2070–2077. 21. DeFronzo RA, Tobin JD, Andres R (1979) Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol 237: E214–E223. 22. Plomgaard P, Bouzakri K, Krogh-Madsen R, Mittendorfer B, Zierath JR, et al. (2005) Tumor necrosis factor-alpha induces skeletal muscle insulin resistance in healthy human subjects via inhibition of Akt substrate 160 phosphorylation. Diabetes 54: 2939–2945. 23. Thomas JA, Schlender KK, Larner J (1968) A rapid filter paper assay for UDPglucose-glycogen glucosyltransferase, including an improved biosynthesis of UDP-14C-glucose. Anal Biochem 25: 486–499. 24. Haugaard SB, Andersen O, Madsbad S, Frosig C, Iversen J, et al. (2005) Skeletal Muscle Insulin Signaling Defects Downstream of Phosphatidylinositol 3-Kinase at the Level of Akt Are Associated With Impaired Nonoxidative Glucose Disposal in HIV Lipodystrophy. Diabetes 54: 3474–3483. 25. Boden G, Jadali F, White J, Liang Y, Mozzoli M, et al. (1991) Effects of fat on insulin-stimulated carbohydrate metabolism in normal men. J Clin Invest 88: 960–966. 26. Mashili FL, Austin RL, Deshmukh AS, Fritz T, Caidahl K, et al. (2011) Direct effects of FGF21 on glucose uptake in human skeletal muscle: implications for type 2 diabetes and obesity. Diabetes Metab Res Rev 27: 286–297. 27. Torriani M, Thomas BJ, Barlow RB, Librizzi J, Dolan S, et al. (2006) Increased intramyocellular lipid accumulation in HIV-infected women with fat redistribution. J Appl Physiol 100: 609–614. 28. Lee MS, Choi SE, Ha ES, An SY, Kim TH, et al. (2012) Fibroblast growth factor-21 protects human skeletal muscle myotubes from palmitate-induced insulin resistance by inhibiting stress kinase and NF-kappaB. Metabolism . 29. Tyynismaa H, Carroll CJ, Raimundo N, Ahola-Erkkila S, Wenz T, et al. (2010) Mitochondrial myopathy induces a starvation-like response. Hum Mol Genet 19: 3948–3958. 30. Maagaard A, Holberg-Petersen M, Kollberg G, Oldfors A, Sandvik L, et al. (2006) Mitochondrial (mt)DNA changes in tissue may not be reflected by depletion of mtDNA in peripheral blood mononuclear cells in HIV-infected patients. Antivir Ther 11: 601–608. 31. Payne BA, Wilson IJ, Hateley CA, Horvath R, Santibanez-Koref M, et al. (2011) Mitochondrial aging is accelerated by anti-retroviral therapy through the clonal expansion of mtDNA mutations. Nat Genet 43: 806–810. 32. Kliewer SA, Mangelsdorf DJ (2010) Fibroblast growth factor 21: from pharmacology to physiology. Am J Clin Nutr 91: 254S–257S. 33. Gallego-Escuredo JM, Domingo P, Gutierrez MD, Mateo MG, Cabeza MC, et al. (2012) Reduced Levels of Serum FGF19 and Impaired Expression of Receptors for Endocrine FGFs in Adipose Tissue From HIV-Infected Patients. J Acquir Immune Defic Syndr 61: 527–534. 34. Domingo P, Gallego-Escuredo JM, Domingo JC, Gutierrez MM, Mateo MG, et al. (2010) Serum FGF21 levels are elevated in association with lipodystrophy, insulin resistance and biomarkers of liver injury in HIV-1-infected patients. AIDS 24: 2629–2637. PLOS ONE | www.plosone.org 7 March 2013 | Volume 8 | Issue 3 | e55632
Pediatric dual-energy X-ray absorptiometry: interpretation and clinical and research application
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Pediatric dual-energy X-ray absorptiometry: interpretation and clinical and research application

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