Abstract
As highlighted in earlier chapters, diabetes mellitus is associated with an increased risk for low-trauma fractures [1, 2]. In the case of Type 1 diabetes (T1DM) this is at least partially mediated through lower bone mineral density (BMD) as reflected by routine clinical measurements such as dual energy X-ray absorptiometry (DXA) [2]. The situation with Type 2 diabetes (T2DM) is clearly more complicated since BMD measurements are typically increased. In view of the differences in underlying pathophysiology and how this may mediate its effects on subsequent fractures, it is unlikely that a single approach for fracture risk assessment will be equally applicable to T1DM and T2DM. Given the preponderance of T2DM among older individuals, the segment of the population at highest risk for osteoporotic fractures, and the BMD-fracture paradox alluded to earlier, this chapter will emphasize considerations in T2DM.
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References
Janghorbani M, Van Dam RM, Willett WC, Hu FB. Systematic review of type 1 and type 2 diabetes mellitus and risk of fracture. Am J Epidemiol. 2007;166:495–505.
Vestergaard P. Discrepancies in bone mineral density and fracture risk in patients with type 1 and type 2 diabetes—a meta-analysis. Osteoporos Int. 2007;18:427–44.
Kanis JA, Melton III LJ, Christiansen C, Johnston CC, Khaltaev N. The diagnosis of osteoporosis. J Bone Miner Res. 1994;9:1137–41.
Looker AC, Wahner HW, Dunn WL, et al. Updated data on proximal femur bone mineral levels of US adults. Osteoporos Int. 1998;8:468–89.
Kanis JA, McCloskey EV, Johansson H, Oden A, Melton III LJ, Khaltaev N. A reference standard for the description of osteoporosis. Bone. 2008;42:467–75.
Siris ES, Chen YT, Abbott TA, et al. Bone mineral density thresholds for pharmacological intervention to prevent fractures. Arch Intern Med. 2004;164:1108–12.
Schuit SC, van der Klift M, Weel AE, et al. Fracture incidence and association with bone mineral density in elderly men and women: the Rotterdam Study. Bone. 2004;34:195–202.
Stone KL, Seeley DG, Lui LY, et al. BMD at multiple sites and risk of fracture of multiple types: long-term results from the Study of Osteoporotic Fractures. J Bone Miner Res. 2003;18:1947–54.
Cranney A, Jamal SA, Tsang JF, Josse RG, Leslie WD. Low bone mineral density and fracture burden in postmenopausal women. CMAJ. 2007;177:575–80.
Rubin KH, Friis-Holmberg T, Hermann AP, Abrahamsen B, Brixen K. Risk assessment tools to identify women with increased risk of osteoporotic fracture: complexity or simplicity? A systematic review. J Bone Miner Res. 2013;28:1701–17.
Whiting P, Rutjes AW, Reitsma JB, Bossuyt PM, Kleijnen J. The development of QUADAS: a tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews. BMC Med Res Methodol. 2003;3:25.
Steyerberg EW. Clinical prediction models: a practical approach to development, validation, and updating. New York: Springer; 2008.
Altman DG, Vergouwe Y, Royston P, Moons KG. Prognosis and prognostic research: validating a prognostic model. BMJ. 2009;338:1432–5.
Royston P, Moons KG, Altman DG, Vergouwe Y. Prognosis and prognostic research: developing a prognostic model. BMJ. 2009;338:1373–7.
Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21:128–38.
Leslie WD, Lix LM. Comparison between various fracture risk assessment tools. Osteoporos Int. 2014;25:1–21.
Cadarette SM, Jaglal SB, Kreiger N, McIsaac WJ, Darlington GA, Tu JV. Development and validation of the Osteoporosis Risk Assessment Instrument to facilitate selection of women for bone densitometry. CMAJ. 2000;162:1289–94.
Koh LK, Sedrine WB, Torralba TP, et al. A simple tool to identify asian women at increased risk of osteoporosis. Osteoporos Int. 2001;12:699–705.
Lydick E, Cook K, Turpin J, Melton M, Stine R, Byrnes C. Development and validation of a simple questionnaire to facilitate identification of women likely to have low bone density. Am J Manag Care. 1998;4:37–48.
Morin S, Tsang JF, Leslie WD. Weight and body mass index predict bone mineral density and fractures in women aged 40 to 59 years. Osteoporos Int. 2009;20:363–70.
Gourlay ML, Powers JM, Lui LY, Ensrud KE. Clinical performance of osteoporosis risk assessment tools in women aged 67 years and older. Osteoporos Int. 2008;19:1175–83.
Rud B, Hilden J, Hyldstrup L, Hrobjartsson A. The Osteoporosis Self-Assessment Tool versus alternative tests for selecting postmenopausal women for bone mineral density assessment: a comparative systematic review of accuracy. Osteoporos Int. 2009;20:599–607.
Schwartz EN, Steinberg DM. Prescreening tools to determine who needs DXA. Curr Osteoporos Rep. 2006;4:148–52.
Cadarette SM, McIsaac WJ, Hawker GA, et al. The validity of decision rules for selecting women with primary osteoporosis for bone mineral density testing. Osteoporos Int. 2004;15:361–6.
Cadarette SM, Jaglal SB, Murray TM, McIsaac WJ, Joseph L, Brown JP. Evaluation of decision rules for referring women for bone densitometry by dual-energy X-ray absorptiometry. JAMA. 2001;286:57–63.
Kanis JA, Oden A, Johansson H, Borgstrom F, Strom O, McCloskey E. FRAX and its applications to clinical practice. Bone. 2009;44:734–43.
Kanis JA, on behalf of the World Health Organization Scientific Group. (2007). Assessment of osteoporosis at the primary health-care level. Technical Report. Published by the University of Sheffield. http://www.shef.ac.uk/FRAX/pdfs/WHO_Technical_Report.pdf
Kanis JA, McCloskey E, Johansson H, Oden A, Leslie WD. FRAX((R)) with and without bone mineral density. Calcif Tissue Int. 2012;90:1–13.
Leslie WD, Morin S, Lix LM, et al. Fracture risk assessment without bone density measurement in routine clinical practice. Osteoporos Int. 2012;23:75–85.
Leslie WD, Lix LM, Johansson H, Oden A, McCloskey E, Kanis JA. Spine-hip discordance and fracture risk assessment: a physician-friendly FRAX enhancement. Osteoporos Int. 2011;22:839–47.
Kanis JA, Johansson H, Oden A, McCloskey EV. Guidance for the adjustment of FRAX according to the dose of glucocorticoids. Osteoporos Int. 2011;22:809–16.
Satagopan JM, Ben-Porat L, Berwick M, Robson M, Kutler D, Auerbach AD. A note on competing risks in survival data analysis. Br J Cancer. 2004;91:1229–35.
Leslie WD, Lix LM, Wu X. Competing mortality and fracture risk assessment. Osteoporos Int. 2013;24:681–8.
Kanis JA, Oden A, McCloskey EV, Johansson H, Wahl DA, Cooper C. A systematic review of hip fracture incidence and probability of fracture worldwide. Osteoporos Int. 2012;23:2239–56.
Kanis JA, Johnell O, Oden A, et al. Long-term risk of osteoporotic fracture in Malmo. Osteoporos Int. 2000;11:669–74.
Kanis JA, Oden A, Johnell O, Jonsson B, De Laet C, Dawson A. The burden of osteoporotic fractures: a method for setting intervention thresholds. Osteoporos Int. 2001;12:417–27.
Lam A, Leslie WD, Lix LM, Yogendran M, Morin SN, Majumdar SR. Major osteoporotic to hip fracture ratios in canadian men and women with Swedish comparisons: a population-based analysis. J Bone Miner Res. 2014;29:1067–73.
Kanis JA, Oden A, Johnell O, et al. The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women. Osteoporos Int. 2007;18:1033–46.
Kanis JA, Oden A, Johansson H, McCloskey E. Pitfalls in the external validation of FRAX. Osteoporos Int. 2012;23:423–31.
Nguyen ND, Frost SA, Center JR, et al. Development of a nomogram for individualizing hip fracture risk in men and women. Osteoporos Int. 2007;18:1109–17.
Nguyen ND, Frost SA, Center JR, et al. Development of prognostic nomograms for individualizing 5-year and 10-year fracture risks. Osteoporos Int. 2008;19:1431–44.
Langsetmo L, Nguyen TV, Nguyen ND, et al. Independent external validation of nomograms for predicting risk of low-trauma fracture and hip fracture. CMAJ. 2011;183:E107–14.
Hippisley-Cox J, Coupland C. Predicting risk of osteoporotic fracture in men and women in England and Wales: prospective derivation and validation of QFractureScores. BMJ. 2009;339:b4229.
Hippisley-Cox J, Coupland C. Derivation and validation of updated QFracture algorithm to predict risk of osteoporotic fracture in primary care in the United Kingdom: prospective open cohort study. BMJ. 2012;344, e3427.
Collins GS, Mallett S, Altman DG. Predicting risk of osteoporotic and hip fracture in the United Kingdom: prospective independent and external validation of QFractureScores. BMJ. 2011;342:d3651.
Cummins NM, Poku EK, Towler MR, O’Driscoll OM, Ralston SH. Clinical risk factors for osteoporosis in Ireland and the UK: a comparison of FRAX and QFractureScores. Calcif Tissue Int. 2011;89:172–7.
Leslie WD, Morin SN, Lix LM, Majumdar SR. Does diabetes modify the effect of FRAX risk factors for predicting major osteoporotic and hip fracture? Osteoporos Int. 2014;25(12):2817–24.
Johnell O, Kanis JA, Oden A, et al. Predictive value of BMD for hip and other fractures. J Bone Miner Res. 2005;20:1185–94.
Marshall D, Johnell O, Wedel H. Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures. BMJ. 1996;312:1254–9.
Fraser LA, Pritchard J, Ioannidis G, et al. Clinical risk factors for fracture in diabetes: a matched cohort analysis. J Clin Densitom. 2011;14:416–21.
Nielson CM, Srikanth P, Orwoll ES. Obesity and fracture in men and women: an epidemiologic perspective. J Bone Miner Res. 2012;27:1–10.
Compston JE, Flahive J, Hosmer DW, et al. Relationship of weight, height, and body mass index with fracture risk at different sites in postmenopausal women: the Global Longitudinal study of Osteoporosis in Women (GLOW). J Bone Miner Res. 2014;29:487–93.
Gower BA, Casazza K. Divergent effects of obesity on bone health. J Clin Densitom. 2013;16:450–4.
Reid IR. Fat and bone. Arch Biochem Biophys. 2010;503:20–7.
Gonnelli S, Caffarelli C, Nuti R. Obesity and fracture risk. Clin Cases Miner Bone Metab. 2014;11:9–14.
Leslie WD, Orwoll ES, Nielson CM, et al. Estimated lean mass and fat mass differentially affect femoral bone density and strength index but are not FRAX independent risk factors for fracture. J Bone Miner Res. 2014;29:2511–9.
Ho-Pham LT, Nguyen UD, Nguyen TV. Association between lean mass, fat mass, and bone mineral density: a meta-analysis. J Clin Endocrinol Metab. 2014;99:30–8.
Johansson H, Kanis JA, Oden A, et al. A meta-analysis of the association of fracture risk and body mass index in women. J Bone Miner Res. 2014;29:223–33.
Nielson CM, Bouxsein ML, Freitas SS, Ensrud KE, Orwoll ES. Trochanteric soft tissue thickness and hip fracture in older men. J Clin Endocrinol Metab. 2009;94:491–6.
Viljakainen HT, Pekkinen M, Saarnio E, Karp H, Lamberg-Allardt C, Makitie O. Dual effect of adipose tissue on bone health during growth. Bone. 2011;48:212–7.
Chan MY, Frost SA, Center JR, Eisman JA, Nguyen TV. Relationship between body mass index and fracture risk is mediated by bone mineral density. J Bone Miner Res. 2014;29(11):2327–35.
Melton III LJ, Riggs BL, Leibson CL, et al. A bone structural basis for fracture risk in diabetes. J Clin Endocrinol Metab. 2008;93:4804–9.
Ishii S, Cauley JA, Crandall CJ, et al. Diabetes and femoral neck strength: findings from the hip strength across the Menopausal Transition Study. J Clin Endocrinol Metab. 2012;97:190–7.
Sornay-Rendu E, Boutroy S, Vilayphiou N, Claustrat B, Chapurlat RD. In obese postmenopausal women, bone microarchitecture and strength are not commensurate to greater body weight: the Os des Femmes de Lyon (OFELY) Study. J Bone Miner Res. 2013;28:1679–87.
Premaor M, Parker RA, Cummings S, et al. Predictive value of FRAX for fracture in obese older women. J Bone Miner Res. 2013;28:188–95.
Adami S. Bone health in diabetes: considerations for clinical management. Curr Med Res Opin. 2009;25:1057–72.
Jackuliak P, Payer J. Osteoporosis, fractures, and diabetes. Int J Endocrinol. 2014;2014:820615.
Leslie WD, Lix LM, Prior HJ, Derksen S, Metge C, O’Neil J. Biphasic fracture risk in diabetes: a population-based study. Bone. 2007;40:1595–601.
Melton III LJ, Leibson CL, Achenbach SJ, Therneau TM, Khosla S. Fracture risk in type 2 diabetes: update of a population-based study. J Bone Miner Res. 2008;23:1334–42.
Schwartz AV, Vittinghoff E, Bauer DC, et al. Association of BMD and FRAX score with risk of fracture in older adults with type 2 diabetes. JAMA. 2011;305:2184–92.
Giangregorio LM, Leslie WD, Lix LM, et al. FRAX underestimates fracture risk in patients with diabetes. J Bone Miner Res. 2012;27:301–8.
Carnevale V, Morano S, Fontana A, et al. Assessment of fracture risk by the FRAX algorithm in men and women with and without type 2 diabetes mellitus: a cross-sectional study. Diabetes Metab Res Rev. 2014;30:313–22.
Bhattoa HP, Onyeka U, Kalina E, et al. Bone metabolism and the 10-year probability of hip fracture and a major osteoporotic fracture using the country-specific FRAX algorithm in men over 50 years of age with type 2 diabetes mellitus: a case-control study. Clin Rheumatol. 2013;32:1161–7.
National Osteoporosis Foundation. Clinician’s guide to prevention and treatment of osteoporosis. Osteoporos Int. 2014;25(10):2359–81.
Leslie WD, Rubin MR, Schwartz AV, Kanis JA. Type 2 diabetes and bone. J Bone Miner Res. 2012;27:2231–7.
Pothuaud L, Barthe N, Krieg MA, Mehsen N, Carceller P, Hans D. Evaluation of the potential use of trabecular bone score to complement bone mineral density in the diagnosis of osteoporosis: a preliminary spine BMD-matched, case-control study. J Clin Densitom. 2009;12:170–6.
Bousson V, Bergot C, Sutter B, Levitz P, Cortet B. Trabecular bone score (TBS): available knowledge, clinical relevance, and future prospects. Osteoporos Int. 2011;23:1489–501.
Hans D, Barthe N, Boutroy S, Pothuaud L, Winzenrieth R, Krieg MA. Correlations between trabecular bone score, measured using anteroposterior dual-energy X-ray absorptiometry acquisition, and 3-dimensional parameters of bone microarchitecture: an experimental study on human cadaver vertebrae. J Clin Densitom. 2011;14:302–12.
Roux JP, Wegrzyn J, Boutroy S, Hans D, Chapurlat R. Relationship between trabecular bone score (TBS), bone mass and bicroarchitecture in human vertebrae: an ex vivo study. Osteoporos Int. 2012;23:S327.
Silva BC, Leslie WD, Resch H, et al. Trabecular bone score: a noninvasive analytical method based upon the DXA image. J Bone Miner Res. 2014;29:518–30.
Leslie WD, Aubry-Rozier B, Lamy O, Hans D. TBS (trabecular bone score) and diabetes-related fracture risk. J Clin Endocrinol Metab. 2013;98:602–9.
Dhaliwal R, Cibula D, Ghosh C, Weinstock RS, Moses AM. Bone quality assessment in type 2 diabetes mellitus. Osteoporos Int. 2014;25:1969–73.
Rubin M, Shah A, Zhang C, et al. Trabecular bone assessment in type 2 diabetes mellitus. J Bone Miner Res. 2013;28 Suppl 1:S398.
Leslie WD, Krieg MA, Hans D. Clinical factors associated with trabecular bone score. J Clin Densitom. 2013;16:374–9.
Leslie WD, Winzenrieth R, Majumdar SR, Lix LM, Hans D. Clinical performance of an updated version of trabecular bone score in men and women: The Manitoba BMD Cohort. J Bone Miner Res. 2014;28 Suppl 1:S297.
Leib E, Winzenrieth R, Aubry-Rozier B, Hans D. Vertebral microarchitecture and fragility fracture in men: a TBS study. Bone. 2014;62:51–5.
Leslie WD, Aubry-Rozier B, Lix LM, Morin SN, Majumdar SR, Hans D. Spine bone texture assessed by trabecular bone score (TBS) predicts osteoporotic fractures in men: the Manitoba Bone Density Program. Bone. 2014;67:10–4.
Leslie WD, Johansson H, Kanis JA, et al. Lumbar spine texture enhances 10-year fracture probability assessment. Osteoporos Int. 2014;25:2271–7.
Farr JN, Drake MT, Amin S, Melton III LJ, McCready LK, Khosla S. In vivo assessment of bone quality in postmenopausal women with type 2 diabetes. J Bone Miner Res. 2014;29:787–95.
Starup-Linde J, Eriksen SA, Lykkeboe S, Handberg A, Vestergaard P. Biochemical markers of bone turnover in diabetes patients-a meta-analysis, and a methodological study on the effects of glucose on bone markers. Osteoporos Int. 2014;25:1697–708.
Saito M, Kida Y, Kato S, Marumo K. Diabetes, collagen, and bone quality. Curr Osteoporos Rep. 2014;12:181–8.
Disclosures
WDL (all fees paid to facility): Speaker bureau: Amgen, Eli Lilly, Novartis. Research grants: Amgen, Genzyme. SH: None.
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Leslie, W.D., Hough, S. (2016). Fracture Risk Assessment in Diabetes. In: Lecka-Czernik, B., Fowlkes, J. (eds) Diabetic Bone Disease. Springer, Cham. https://doi.org/10.1007/978-3-319-16402-1_3
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