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Ethnicity and Fracture Risk Assessment: An Issue That Is More Than Skin Deep
Journal of Clinical Densitometry, vol. 9, no. 4, 406e412, 2006 Ó Copyright 2006 by The International Society for Clinical Densitometry 1094-6950/06/9:406e412/$32.00 DOI: 10.1016/j.jocd.2006.07.003
Original Article
Race/Ethnicity and Fracture Risk Assessment: An Issue That Is More Than Skin Deep William D. Leslie*,1 and Brian Lentle2 1
2
Departments of Medicine and Radiology, University of Manitoba, Winnipeg, Manitoba; and Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
Abstract The study of race/ethnicity in relation to health outcomes and health disparities is of great importance in medicine. This is as true in the field of osteoporosis as it is in others, and it comes into sharp focus when the question of population-specific reference data for bone densitometry arises. Race/ethnicity can be viewed as both an ecosocial construct and as a biomedical concept. Whether or not, and how, to use race/ethnicity in fracture assessment potentially places these two paradigms in opposition. In this article, some of the issues that need to be considered to develop a rational approach to reference data selection and a globally acceptable measure of fracture risk are reviewed. Race/ethnicity is often a proxy for other disease-related risk factors. Understanding fundamental risk factors goes beyond the language of race/ethnicity. Key Words: Osteoporosis; fractures; race; ethnicity.
be factored into such risk assessment is not yet clear, and in this editorial we examine some of the theoretical and practical issues that need to be considered.
Introduction Few would disagree that race/ethnicity is a social determinant that can have an impact on health outcomes, but attempts to use these constructs as the basis for interpreting bone density results raise immediate concerns. The current International Society for Clinical Densitometry position is to use a uniform white (non-race adjusted) female normative database for women and men of all ethnic groups, but this statement is intended to apply only to the United States (US) population (1,2). Attempts to develop a position on population-specific reference data that would be acceptable throughout the rest of the world have failed so far. If successful, an initiative from a world health organization working group to develop a globally applicable measure of absolute fracture risk based on multiple risk factors including bone mineral density (BMD), promises to establish a level playing field for risk assessment (3). However, how race/ethnicity is to
The Language of Race and Ethnicity Although there are many definitions of race, the historical term is rooted in the idea that the human species is divided into distinct groups on the basis of biological and behavioral differences. Nevertheless, genetic studies disprove the existence of biogenetically distinct races (Caucasoid, Negroid, Mongoloid, Australoid) (4). A broader concept of population differences is embodied in the preferred term ‘‘ethnicity’’ (or ethnic identity), which refers to membership in a particular cultural group according to common racial, national, tribal, religious, and linguistic origins and affiliations (5). Integral to both terms is the concept of identity. The notion of self can not be separated from an individual’s place in the social fabric, and it is our membership in a social group based on shared history, culture, values, and beliefs that contributes to this self-identity. Race/ethnicity is like the proverbial oniondit has many layers of subtlety. Historical labels (e.g., white, black, Asian, Hispanic) belie complex interactions between genetic factors, environment, language, and culture.
Received 01/27/06; Revised 07/08/06; Accepted 07/14/06. *Address correspondence to: William D. Leslie, Department of Medicine, (C5121), 409 Tache Ave, Winnipeg, Manitoba R2H 2A6, Canada. E-mail: [email protected]
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Race/Ethnicity and Fracture Risk: Theoretical Considerations Other health disciplines are also grappling with the question of how to best use race/ethnicity for identifying disease-causative or disease-preventive factors. Rebbeck et al. (6) recently reviewed the implications of using (or not using) race/ethnicity in studying cancer disparities and genetic epidemiology. Hypertension and diabetes mellitus (i.e., complex multifactorial disorders that mediate their racial/ethnicity effects at least partially through social mechanisms) may offer a good model for considering population differences in fracture risk, which are also strongly influenced by environmental factors (3). Genetic epidemiology has not revealed unique genotypes that explain the disparate burden of hypertension in African Americans, and the message has emerged that environmental factors (i.e., social and behavioral) predominate in their effect on cardiovascular risk (7,8). Similarly, African Americans and some other ethnic minority groups suffer disproportionately from type 2 diabetes and its complications compared with white Americans, with both genetic and environmental factors contributing to disease and its impact (9). Notably much of the racial/ethnic difference in morbidity from diabetic complications disappears when whites and nonwhites establish comparable degrees of glycemic control (10). The need for population-specific reference data to calculate densitometry-derived T-scores is highly controversial (1). In part, this derives from differences in fracture rates and bone density that had been observed between countries Journal of Clinical Densitometry
and different ethnic groups, with a concern that T-scores derived from a single reference population (usually taken to be whites) may be inappropriate (11e15). On the other hand, the need to develop specific reference data for every combination of country, race/ethnicity, and gender on every bone density instrument is virtually impossible. In fact, even within a relatively homogeneous population, variations in bone density and fractures rates can be observed (15). To follow this argument to its logical conclusion would require reference data for each subgroup within a country that is found to have a different mean bone density and/or fracture rate at any given age, an obvious source of clinical confusion. Also, the economic implications can not be ignored, given cultural differences in health priorities and regional inequalities in health care resources (16). Population-specific reference data may be unnecessary in many (and probably most) cases. Consider a hypothetical population (P) with a young adult mean BMD of 1.0 g/cm2 and standard deviation (SD) of 0.1 g/cm2. For simplicity, we will assume that fracture risk for this population doubles for each SD change in bone density. This results in a bone density-fracture risk curve as shown in Fig. 1. All other factors being equal, as an individual’s bone density decreases (moving from B to A), there is an exponential increase in fracture risk as the individual ascends the fracture risk curve. Conversely, higher bone density (moving from B to C) is associated with a descent on the fracture risk curve. In this example, B has twice the fracture risk of C, whereas A has 4 times the fracture risk of C. The T-scores of these hypothetical individuals (using population P reference data) would be 3.5 for A, 2.5 for B, and 1.5 for C, which appropriately reflects their relative fracture risks. Now consider two other populations, P1 with bone density measurements that average 0.1 g/cm2 lower and P2 with bone
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The US Census 2000 identified 5 primary race categories with an additional 58 combinations. Race is combined with Hispanic or non-Hispanic origin to create 126 distinct raciale ethnic categories. Although no subcategories were defined for white, 5 subgroups were identified for Alaskan natives, 36 tribal subgroups for American Indians, 17 subgroups for Asians, 12 detailed native Hawaiian and Pacific Islander subgroups, and 28 Hispanic or Latino categories. This enormous cultural diversity within the US is paralleled throughout the world. The People’s Republic of China officially recognizes 56 nationalities within its borders. Racial and ethnic diversity is influenced by acculturation, migration, and intergroup marriages. There has been no systematic attempt to enumerate all ethnic groups in the world, but clearly this number would be in the hundreds if not thousands. The admittedly vague definition of ethnicity challenges researchers who wish to study its effect on bone density and fracture rates. Continentally defined populations are not homogeneous and can be subdivided along national, religious, linguistic, and other lines. Although the terms (i.e., race and ethnicity) are often defined or interpreted as measuring different aspects of group identity (i.e., biological vs. social), it has been argued that both are social constructs and are associated with a range of biological and social variables (4). As the terms are often interchangeably used in stereotyping social groups, the term ‘‘race/ethnicity’’ will be used here to avoid drawing any unnecessary distinctions between the two.
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Fig. 1. Three hypothetical individuals from populations with the same bone density-fracture risk relationship. Volume 9, 2006
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density measurements that average 0.1 g/cm2 higher than in the population (P). In general, differences in bone density between countries and ethnic groups are less dramatic than these. Furthermore, assume that these individuals have precisely the same density-fracture relationship as in Fig. 1. In this case, it is only the individual’s absolute bone density measurement that is important in terms of fracture risk. Essentially, fractures are ‘‘blind’’ to whether the individual is a member of population P, P1, or P2. Of course, if population-specific T-scores were derived, then these would be identical (i.e., T-score of 2.5 for A based on population P1 reference data and T-score of 2.5 for C when based on population P2 reference data). If the intent is to define an individual’s fracture risk, then this use of population-specific T-scores is clearly not desirable. Alternatively, consider that populations P1 and P2 have different density-fracture relationships as illustrated in Fig. 2. In this example, it is assumed that population P1 has one-half the fracture rate seen in population P at each level of absolute bone density, whereas population P2 has twice the fracture rate at each level of bone density. The use of populationspecific reference data may be appropriate in this situation, because it generates equal T-scores and more accurately reflects that the individuals have similar fracture risk. Of course, this assumes that the difference in fracture rates between populations is precisely matched by differences in average bone density between the populations. This may or may not be the case, and underscores the need to fully characterize the density-fracture relationship, and not simply fracture rates or bone density in isolation. The situation is compounded by the potential for skeletal size to influence bone density because dual-energy X-ray
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Fig. 2. Three hypothetical individuals from populations with different bone density-fracture risk relationships. A, belongs to population P1; B, belongs to population P; and C, belongs to population P2. Journal of Clinical Densitometry
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Fig. 3. A proposed model for how race/ethnicity can affect osteoporotic fracture risk. In this formulation, race/ethnicity is depicted as a risk factor for risk factors. absorptiometry (DXA)-derived measurements of BMD are expressed in areal density (g/cm2), an artificial construct that does not take into account the variation in the dimension of bone in the orthogonal plane.
Race/Ethnicity and Fracture Risk: Practical Considerations The use of population-specific reference data is complex, and use of population-specific T-scores must consider both the distribution of bone density values and the nature of the density-fracture relationship. In general, scientific data describing the latter are limited. Most of the available evidence indicates that on average blacks have higher bone density measurements and lower fracture rates (11,12). This would be consistent with a density-fracture relationship similar to whites as depicted in Fig. 1. Similarly, differences in vertebral fracture rates in European countries are largely explained on the basis of differences in mean BMD and age (15). Limited multi-ethnic fracture data indicate that the relative risk for fracture per SD reduction in BMD is similar, even when absolute risk is different (11,17). This is consistent with the analysis previously presented (i.e., a doubling of fracture risk for each SD change in bone density for populations P, P1, and P2 with different absolute risks). In general, small differences in SD can have large effects on T-scores and the prevalence of low BMD (18,19); therefore, even when population-specific data are considered appropriate, these should be limited to defining the reference mean and not the SD. There are data to suggest that Asians have lower average bone density than whites, but with fracture rates that are no greater or even paradoxically lower (11,20,21). This implies a different density-fracture relationship as seen in Fig. 2, and using the reference data of whites may not be appropriate. Smaller skeletal size appears to be the major factor influencing the paradoxical relationship between BMD and fracture rates in Asians as previously noted (22e26). Migration further complicates the effect of race/ethnicity due to changes in lifestyle and diet. Kin et al. (27) found that US-born Japanese-American women had higher body fat than immigrant Japanese-American women, who in turn had higher BMD and higher body fat than their native Japanese counterparts. Weight, exercise, early menarche, and years of lifetime Volume 9, 2006
Race/Ethnicity and Fracture Risk Assessment estrogen exposure correlated positively with BMD. United States-born Japanese-American women had BMD values equivalent to whites, and no significant total-body BMD differences were found among the three groups after adjustment for age, height, and weight. Furthermore, the racial size disparity is actually diminishing due to secular changes in growth patterns (28,29). Therefore, even Asian reference data derived from contemporary young Asian adults may not be applicable across the full age spectrum. Volumetric adjustments (e.g., bone mineral apparent density [BMAD]) may be helpful for Asians, because it addresses the cause of the disparity and not merely the symptom (22,23). The multiethnic US Study of Women’s Health Across the Nation (SWAN) shows that reliance on a single factor, such as BMAD, may not be sufficient (26). When spine and femoral neck BMAD values were also adjusted for differences in weight, Chinese women had significantly higher values than whites, suggesting that Asians may have lower fracture rates than whites simply because they have higher bone density when adjusting for the effects of bone size and body weight. The BMAD approach does not improve fracture risk prediction in older white women (30), a relatively homogeneous population. In groups that differ in size, such as between men and women, it has been proposed that measures of bone mass that correct for bone size may yield universal estimates of fracture risk (31). The BMAD measurements can be achieved for the lumbar spine and total hip with levels of precision as good as for areal BMD, although not for the femoral neck (32). Wu et al. (33) studied 1426 healthy Asian women (aged 20 e 56 yr) and showed a strong association between height, bone area, and bone mineral content confounded detection of osteoporosis when derived from spine areal BMD (i.e., small bone area group prevalence, 4.5%; intermediate bone area group, 1.3%; and large bone area group, 0%), whereas there was no significant difference in the detection rates among the groups using volumetric BMD. The impact of bone size, when planar bone densitometry is used in the diagnosis of osteoporosis, extends to other ethnic groups having been described in Indians of Mayan descent from southern Mexico (34), Pakistani Muslims, and Gujarati Hindus living in the United Kingdom (24) and Inuit from northern Greenland (35).
Getting Beyond Race/Ethnicity The enormous global variation in fracture rates dwarfs the population differences in mean BMD that have been reported (13,36). It follows that population-specific reference data can not resolve the inherent discordance in fracture rates, and in some cases (as previously noted) it may even exacerbate the problem. Even if we lived in a world where there were no race/ethnicity-related differences in bone density, other components of the race/ethnicity complex (e.g., prevalence of smoking) might continue to affect fracture rates. In fact, the use of population-specific reference data may lead to a false sense that the problem has been ‘‘fixed’’ and become Journal of Clinical Densitometry
409 a distraction from developing what is neededda new fracture risk paradigm. The evolution from statements of relative fracture risk (as epitomized by the T-score) to absolute fracture (based on multiple risk factors) is challenging old concepts (3,37,38). Conceptually, basal absolute fracture risk (A0) based on some set of parameters (e.g., age, gender, and proximal femoral T-score) would be modified by additional independent relative risk factors (R1, R2, R3, . Rn) to derive a final absolute risk estimate (Af which is approximated by A0 x R1 x R2 x R3 .x. Rn). If, as evidence to date appears to suggest, population differences in Af are largely due to differences in A0 with similar relative risk factors, R1, R2, R3, . Rn, then population-specific differences in fracture rates could be accommodated with the addition of a single additional calibration factor. The evolving cardiovascular risk assessment literature supports this approach. Ten-yr coronary heart disease risk models developed from the US Framingham cohort (39) were found to overestimate risk in some cohorts (40,41), whereas underestimating risk in socioeconomically deprived individuals (42). A simple ‘‘recalibration’’ factor appeared to resolve the discrepancy as the relative difference was roughly constant at all levels of risk (40). A corollary to using a population-calibrated risk assessment system for deriving absolute fracture risk is that the choice of reference population becomes arbitrary. For example, we could derive fracture risk for individual A from population P1 (Fig. 2) using reference data for P1. Alternatively, we could also derive fracture risk for individual A using reference data for population P2. Recall that T-scores using population P2 reference data would be 2 SD lower than using population P2 reference data (equivalent to a four-fold increase in the densitometric relative risk term assuming a doubling in fracture risk for each SD). Providing that the calibration factor is re-scaled to compensate for this systematic difference (i.e., R0 using population P2 reference data should be one-quarter the R0 for population P1 reference data), then the risk calculations become mathematically equivalent. By extension, BMD reference data from any population could probably be used for absolute fracture risk assessment in any other population. One recent study has specifically examined the interaction of race/ethnicity with chronic disease risk factors for osteoporotic fracture (43). The populations that were studied (aboriginal and non-aboriginal Canadians) had large differences in fracture rates and chronic disease burden. Greater prevalence of diabetes, comorbidity, and substance abuse contributed to higher rates of fracture in the aboriginal population, but the relative risk of fracture for these factors was similar. In this analysis, race/ethnicity was essentially a risk factor for other risk factors (Fig. 3). As noted earlier, selection of risk factors that are causally linked to the observed ethnic differences in osteoporotic fracture rate variations provides a more direct and equitable approach. At the same time, it addresses the contribution of similar factors within an ethnically homogeneous population. For example, if BMAD is adopted as an equitable solution to Volume 9, 2006
410 the bone density-fracture paradox in Asians, then it can also benefit whites with small skeletal size (44,45). Two articles in a recent issue of the Journal of Clinical Densitometry illustrate this principle. Andersen et al. (35) found significantly lower heel BMD in Arctic Inuit compared with whites ( p ! 0.001), but whites were significantly taller and heavier. The ethnic difference in BMD disappeared when adjusted for body size. Lage at el. (46) compared DXA-derived areal and volumetric BMD measurements in 62 females with Turner’s syndrome (a genetic disorder characterized by short stature) with BMD in 102 normal females. Most of the females with Turner’s syndrome (83.8%) had a Z-score below 1 for areal BMD (of which 41.9% had Z-scores below 2.5), whereas the majority (58.1%) had a volumetric BMD Z-score above 1 (of which only 12.9% were below 2.5). Other studies have confirmed the confounding effect of size on assessment of BMD in Turner’s syndrome (47,48). Although the two populations studied were quite different, both studies gave the same message: BMD measurements that did not account for body size underestimated bone mass and potentially overestimated skeletal fragility. Similarly, where fracture risk in an ethnic group is attributable to geography, nutrition, or comorbid illness, then inclusion of these factors (either directly or indirectly) creates a model that is more transparent and applicable to the larger population, and avoids the use of race/ethnicity as a proxy variable. It has been argued that race/ethnicity should only be used when it is the only available proxy for important unmeasured variables (4). For example, several measures of socioeconomic status have been associated with BMD (49e51) and fracture risk (52e54). It would be clearly inappropriate to suggest a need for social class-specific reference data for BMD measurements.
Conclusions Race/ethnicity can be seen as both an ecosocial construct and a biomedical descriptor. When viewed as the former, issues of population-specific reference data become much less important. Others will emphasize the biological aspects of race/ethnicity. Both perspectives can be supported, and only more scientific study and dialogue will clarify their relative importance. Much more research is required to better characterize the nature of the density-fracture relationship in relation to gender, geography/environment, and race/ethnicity. Use of a common reference population enhances consistent reporting and reduces confusion, and would probably be applicable to most (if not all) populations, providing there are appropriate steps to calibrate the fracture prediction model. White reference data are the most complete and best validated as a tool for fracture prediction and treatment initiation, but this is no way implies that white reference data are ‘‘best’’ or that other reference data (including a globally pooled reference population) would not serve equally well. Where different populations share similar density-fracture relationships, this would imply the use of the common reference population (i.e., no adjustment for country or race/ethnicity). Journal of Clinical Densitometry
Leslie and Lentle Even in regions where there is strong scientific evidence to support an alteration in the normal density-fracture relationship, the use of a common reference population may still be satisfactory, providing that there is an appropriate calibration factor or an equivalent approach that directly addresses the underlying etiology for the discordance (e.g., skeletal size and weight adjustment for Asian populations). The move toward absolute fracture risk as the basis for intervention will likely neutralize much of the current controversy regarding choice of reference population, because the T-score becomes an intermediate measure on the path linking BMD to fracture risk. Providing that the fracture prediction model has been appropriately calibrated, any reference population can be used. As we celebrate our cultural history and heritage as individuals and groups, race/ethnicity presents a challenge to those involved in the field of bone densitometry. The ‘‘right’’ response goes far beyond simply addressing issues related to reference population data, because this can not resolve ethnic disparities in osteoporotic fracture rates. Race/ethnicity is often a proxy for other disease-related risk factors. As Hadler wrote (55): ‘‘After a century of presumption and assumption, modern science is finally deconstructing the construct of ‘race.’ There are genetic differences between populations with differing ancestral continents of origin. However, the genomic similarities are far more striking than the differences. Furthermore, in an outbred population such as the citizenry of the United States, ethnic and racial labeling is on the shakiest of genetic grounds. The label should be used with caution so as not to reinforce untenable stereotypes while to mask the sociocultural variables that are far more influential in terms of longevity and other health outcomes.’’ Understanding the true causal risk factors allows us to get beyond the superficial shorthand that comes with the language of race/ethnicity. Improved understanding can only enhance risk assessment and appropriate intervention.
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for osteoporotic fractures: a comparison in Canadian Aboriginals and non-Aboriginals. Osteoporos Int 17:1358e1368. Benbassat CA, Eshed V, Kamjin M, Laron Z. 2003 Are adult patients with Laron syndrome osteopenic? A comparison between dual-energy X-ray absorptiometry and volumetric bone densities. J Clin Endocrinol Metab 88:4586e4589. Neely EK, Marcus R, Rosenfeld RG, Bachrach LK. 1993 Turner syndrome adolescents receiving growth hormone are not osteopenic. J Clin Endocrinol Metab 76:861e866. Lage AZ, Brandao CA, Mendes JR, et al. 2005 High degree of discordance between three-dimensional and two-dimensional lumbar spine bone mineral density in Turner’s Syndrome. J Clin Densitom 8:461e466. Bakalov VK, Chen ML, Baron J, et al. 2003 Bone mineral density and fractures in Turner syndrome. Am J Med 115: 259e264. Ross JL, Long LM, Feuillan P, Cassorla F, Cutler GB Jr. 1991 Normal bone density of the wrist and spine and increased wrist fractures in girls with Turner’s syndrome. J Clin Endocrinol Metab 73:355e359.
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Leslie and Lentle 49. Clark EM, Ness A, Tobias JH. 2005 Social position affects bone mass in childhood through opposing actions on height and weight. J Bone Miner Res 20:2082e2089. 50. Varenna M, Binelli L, Zucchi F, Ghiringhelli D, Gallazzi M, Sinigaglia L. 1999 Prevalence of osteoporosis by educational level in a cohort of postmenopausal women. Osteoporos Int 9:236e241. 51. Wang MC, Dixon LB. 2006 Socioeconomic influences on bone health in postmenopausal women: findings from NHANES III, 1988-1994. Osteoporos Int 17:91e98. 52. Bacon WE, Hadden WC. 2000 Occurrence of hip fractures and socioeconomic position. J Aging Health 12:193e203. 53. Farahmand BY, Persson PG, Michaelsson K, Baron JA, Parker MG, Ljunghall S. 2000 Socioeconomic status, marital status and hip fracture risk: a population-based case-control study. Osteoporos Int 11:803e808. 54. Leslie WD, Derksen SA, Metge C, et al. 2005 Demographic risk factors for fracture in First Nations people. Can J Public Health 96(Suppl 1):S45eS50. 55. Hadler NM. 2004 The last well person: how to stay well despite the health-care system. Montreal: McGill-Queens University Press.
Volume 9, 2006
Original Article
Race/Ethnicity and Fracture Risk Assessment: An Issue That Is More Than Skin Deep William D. Leslie*,1 and Brian Lentle2 1
2
Departments of Medicine and Radiology, University of Manitoba, Winnipeg, Manitoba; and Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
Abstract The study of race/ethnicity in relation to health outcomes and health disparities is of great importance in medicine. This is as true in the field of osteoporosis as it is in others, and it comes into sharp focus when the question of population-specific reference data for bone densitometry arises. Race/ethnicity can be viewed as both an ecosocial construct and as a biomedical concept. Whether or not, and how, to use race/ethnicity in fracture assessment potentially places these two paradigms in opposition. In this article, some of the issues that need to be considered to develop a rational approach to reference data selection and a globally acceptable measure of fracture risk are reviewed. Race/ethnicity is often a proxy for other disease-related risk factors. Understanding fundamental risk factors goes beyond the language of race/ethnicity. Key Words: Osteoporosis; fractures; race; ethnicity.
be factored into such risk assessment is not yet clear, and in this editorial we examine some of the theoretical and practical issues that need to be considered.
Introduction Few would disagree that race/ethnicity is a social determinant that can have an impact on health outcomes, but attempts to use these constructs as the basis for interpreting bone density results raise immediate concerns. The current International Society for Clinical Densitometry position is to use a uniform white (non-race adjusted) female normative database for women and men of all ethnic groups, but this statement is intended to apply only to the United States (US) population (1,2). Attempts to develop a position on population-specific reference data that would be acceptable throughout the rest of the world have failed so far. If successful, an initiative from a world health organization working group to develop a globally applicable measure of absolute fracture risk based on multiple risk factors including bone mineral density (BMD), promises to establish a level playing field for risk assessment (3). However, how race/ethnicity is to
The Language of Race and Ethnicity Although there are many definitions of race, the historical term is rooted in the idea that the human species is divided into distinct groups on the basis of biological and behavioral differences. Nevertheless, genetic studies disprove the existence of biogenetically distinct races (Caucasoid, Negroid, Mongoloid, Australoid) (4). A broader concept of population differences is embodied in the preferred term ‘‘ethnicity’’ (or ethnic identity), which refers to membership in a particular cultural group according to common racial, national, tribal, religious, and linguistic origins and affiliations (5). Integral to both terms is the concept of identity. The notion of self can not be separated from an individual’s place in the social fabric, and it is our membership in a social group based on shared history, culture, values, and beliefs that contributes to this self-identity. Race/ethnicity is like the proverbial oniondit has many layers of subtlety. Historical labels (e.g., white, black, Asian, Hispanic) belie complex interactions between genetic factors, environment, language, and culture.
Received 01/27/06; Revised 07/08/06; Accepted 07/14/06. *Address correspondence to: William D. Leslie, Department of Medicine, (C5121), 409 Tache Ave, Winnipeg, Manitoba R2H 2A6, Canada. E-mail: [email protected]
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Race/Ethnicity and Fracture Risk: Theoretical Considerations Other health disciplines are also grappling with the question of how to best use race/ethnicity for identifying disease-causative or disease-preventive factors. Rebbeck et al. (6) recently reviewed the implications of using (or not using) race/ethnicity in studying cancer disparities and genetic epidemiology. Hypertension and diabetes mellitus (i.e., complex multifactorial disorders that mediate their racial/ethnicity effects at least partially through social mechanisms) may offer a good model for considering population differences in fracture risk, which are also strongly influenced by environmental factors (3). Genetic epidemiology has not revealed unique genotypes that explain the disparate burden of hypertension in African Americans, and the message has emerged that environmental factors (i.e., social and behavioral) predominate in their effect on cardiovascular risk (7,8). Similarly, African Americans and some other ethnic minority groups suffer disproportionately from type 2 diabetes and its complications compared with white Americans, with both genetic and environmental factors contributing to disease and its impact (9). Notably much of the racial/ethnic difference in morbidity from diabetic complications disappears when whites and nonwhites establish comparable degrees of glycemic control (10). The need for population-specific reference data to calculate densitometry-derived T-scores is highly controversial (1). In part, this derives from differences in fracture rates and bone density that had been observed between countries Journal of Clinical Densitometry
and different ethnic groups, with a concern that T-scores derived from a single reference population (usually taken to be whites) may be inappropriate (11e15). On the other hand, the need to develop specific reference data for every combination of country, race/ethnicity, and gender on every bone density instrument is virtually impossible. In fact, even within a relatively homogeneous population, variations in bone density and fractures rates can be observed (15). To follow this argument to its logical conclusion would require reference data for each subgroup within a country that is found to have a different mean bone density and/or fracture rate at any given age, an obvious source of clinical confusion. Also, the economic implications can not be ignored, given cultural differences in health priorities and regional inequalities in health care resources (16). Population-specific reference data may be unnecessary in many (and probably most) cases. Consider a hypothetical population (P) with a young adult mean BMD of 1.0 g/cm2 and standard deviation (SD) of 0.1 g/cm2. For simplicity, we will assume that fracture risk for this population doubles for each SD change in bone density. This results in a bone density-fracture risk curve as shown in Fig. 1. All other factors being equal, as an individual’s bone density decreases (moving from B to A), there is an exponential increase in fracture risk as the individual ascends the fracture risk curve. Conversely, higher bone density (moving from B to C) is associated with a descent on the fracture risk curve. In this example, B has twice the fracture risk of C, whereas A has 4 times the fracture risk of C. The T-scores of these hypothetical individuals (using population P reference data) would be 3.5 for A, 2.5 for B, and 1.5 for C, which appropriately reflects their relative fracture risks. Now consider two other populations, P1 with bone density measurements that average 0.1 g/cm2 lower and P2 with bone
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The US Census 2000 identified 5 primary race categories with an additional 58 combinations. Race is combined with Hispanic or non-Hispanic origin to create 126 distinct raciale ethnic categories. Although no subcategories were defined for white, 5 subgroups were identified for Alaskan natives, 36 tribal subgroups for American Indians, 17 subgroups for Asians, 12 detailed native Hawaiian and Pacific Islander subgroups, and 28 Hispanic or Latino categories. This enormous cultural diversity within the US is paralleled throughout the world. The People’s Republic of China officially recognizes 56 nationalities within its borders. Racial and ethnic diversity is influenced by acculturation, migration, and intergroup marriages. There has been no systematic attempt to enumerate all ethnic groups in the world, but clearly this number would be in the hundreds if not thousands. The admittedly vague definition of ethnicity challenges researchers who wish to study its effect on bone density and fracture rates. Continentally defined populations are not homogeneous and can be subdivided along national, religious, linguistic, and other lines. Although the terms (i.e., race and ethnicity) are often defined or interpreted as measuring different aspects of group identity (i.e., biological vs. social), it has been argued that both are social constructs and are associated with a range of biological and social variables (4). As the terms are often interchangeably used in stereotyping social groups, the term ‘‘race/ethnicity’’ will be used here to avoid drawing any unnecessary distinctions between the two.
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density measurements that average 0.1 g/cm2 higher than in the population (P). In general, differences in bone density between countries and ethnic groups are less dramatic than these. Furthermore, assume that these individuals have precisely the same density-fracture relationship as in Fig. 1. In this case, it is only the individual’s absolute bone density measurement that is important in terms of fracture risk. Essentially, fractures are ‘‘blind’’ to whether the individual is a member of population P, P1, or P2. Of course, if population-specific T-scores were derived, then these would be identical (i.e., T-score of 2.5 for A based on population P1 reference data and T-score of 2.5 for C when based on population P2 reference data). If the intent is to define an individual’s fracture risk, then this use of population-specific T-scores is clearly not desirable. Alternatively, consider that populations P1 and P2 have different density-fracture relationships as illustrated in Fig. 2. In this example, it is assumed that population P1 has one-half the fracture rate seen in population P at each level of absolute bone density, whereas population P2 has twice the fracture rate at each level of bone density. The use of populationspecific reference data may be appropriate in this situation, because it generates equal T-scores and more accurately reflects that the individuals have similar fracture risk. Of course, this assumes that the difference in fracture rates between populations is precisely matched by differences in average bone density between the populations. This may or may not be the case, and underscores the need to fully characterize the density-fracture relationship, and not simply fracture rates or bone density in isolation. The situation is compounded by the potential for skeletal size to influence bone density because dual-energy X-ray
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Fig. 2. Three hypothetical individuals from populations with different bone density-fracture risk relationships. A, belongs to population P1; B, belongs to population P; and C, belongs to population P2. Journal of Clinical Densitometry
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Fig. 3. A proposed model for how race/ethnicity can affect osteoporotic fracture risk. In this formulation, race/ethnicity is depicted as a risk factor for risk factors. absorptiometry (DXA)-derived measurements of BMD are expressed in areal density (g/cm2), an artificial construct that does not take into account the variation in the dimension of bone in the orthogonal plane.
Race/Ethnicity and Fracture Risk: Practical Considerations The use of population-specific reference data is complex, and use of population-specific T-scores must consider both the distribution of bone density values and the nature of the density-fracture relationship. In general, scientific data describing the latter are limited. Most of the available evidence indicates that on average blacks have higher bone density measurements and lower fracture rates (11,12). This would be consistent with a density-fracture relationship similar to whites as depicted in Fig. 1. Similarly, differences in vertebral fracture rates in European countries are largely explained on the basis of differences in mean BMD and age (15). Limited multi-ethnic fracture data indicate that the relative risk for fracture per SD reduction in BMD is similar, even when absolute risk is different (11,17). This is consistent with the analysis previously presented (i.e., a doubling of fracture risk for each SD change in bone density for populations P, P1, and P2 with different absolute risks). In general, small differences in SD can have large effects on T-scores and the prevalence of low BMD (18,19); therefore, even when population-specific data are considered appropriate, these should be limited to defining the reference mean and not the SD. There are data to suggest that Asians have lower average bone density than whites, but with fracture rates that are no greater or even paradoxically lower (11,20,21). This implies a different density-fracture relationship as seen in Fig. 2, and using the reference data of whites may not be appropriate. Smaller skeletal size appears to be the major factor influencing the paradoxical relationship between BMD and fracture rates in Asians as previously noted (22e26). Migration further complicates the effect of race/ethnicity due to changes in lifestyle and diet. Kin et al. (27) found that US-born Japanese-American women had higher body fat than immigrant Japanese-American women, who in turn had higher BMD and higher body fat than their native Japanese counterparts. Weight, exercise, early menarche, and years of lifetime Volume 9, 2006
Race/Ethnicity and Fracture Risk Assessment estrogen exposure correlated positively with BMD. United States-born Japanese-American women had BMD values equivalent to whites, and no significant total-body BMD differences were found among the three groups after adjustment for age, height, and weight. Furthermore, the racial size disparity is actually diminishing due to secular changes in growth patterns (28,29). Therefore, even Asian reference data derived from contemporary young Asian adults may not be applicable across the full age spectrum. Volumetric adjustments (e.g., bone mineral apparent density [BMAD]) may be helpful for Asians, because it addresses the cause of the disparity and not merely the symptom (22,23). The multiethnic US Study of Women’s Health Across the Nation (SWAN) shows that reliance on a single factor, such as BMAD, may not be sufficient (26). When spine and femoral neck BMAD values were also adjusted for differences in weight, Chinese women had significantly higher values than whites, suggesting that Asians may have lower fracture rates than whites simply because they have higher bone density when adjusting for the effects of bone size and body weight. The BMAD approach does not improve fracture risk prediction in older white women (30), a relatively homogeneous population. In groups that differ in size, such as between men and women, it has been proposed that measures of bone mass that correct for bone size may yield universal estimates of fracture risk (31). The BMAD measurements can be achieved for the lumbar spine and total hip with levels of precision as good as for areal BMD, although not for the femoral neck (32). Wu et al. (33) studied 1426 healthy Asian women (aged 20 e 56 yr) and showed a strong association between height, bone area, and bone mineral content confounded detection of osteoporosis when derived from spine areal BMD (i.e., small bone area group prevalence, 4.5%; intermediate bone area group, 1.3%; and large bone area group, 0%), whereas there was no significant difference in the detection rates among the groups using volumetric BMD. The impact of bone size, when planar bone densitometry is used in the diagnosis of osteoporosis, extends to other ethnic groups having been described in Indians of Mayan descent from southern Mexico (34), Pakistani Muslims, and Gujarati Hindus living in the United Kingdom (24) and Inuit from northern Greenland (35).
Getting Beyond Race/Ethnicity The enormous global variation in fracture rates dwarfs the population differences in mean BMD that have been reported (13,36). It follows that population-specific reference data can not resolve the inherent discordance in fracture rates, and in some cases (as previously noted) it may even exacerbate the problem. Even if we lived in a world where there were no race/ethnicity-related differences in bone density, other components of the race/ethnicity complex (e.g., prevalence of smoking) might continue to affect fracture rates. In fact, the use of population-specific reference data may lead to a false sense that the problem has been ‘‘fixed’’ and become Journal of Clinical Densitometry
409 a distraction from developing what is neededda new fracture risk paradigm. The evolution from statements of relative fracture risk (as epitomized by the T-score) to absolute fracture (based on multiple risk factors) is challenging old concepts (3,37,38). Conceptually, basal absolute fracture risk (A0) based on some set of parameters (e.g., age, gender, and proximal femoral T-score) would be modified by additional independent relative risk factors (R1, R2, R3, . Rn) to derive a final absolute risk estimate (Af which is approximated by A0 x R1 x R2 x R3 .x. Rn). If, as evidence to date appears to suggest, population differences in Af are largely due to differences in A0 with similar relative risk factors, R1, R2, R3, . Rn, then population-specific differences in fracture rates could be accommodated with the addition of a single additional calibration factor. The evolving cardiovascular risk assessment literature supports this approach. Ten-yr coronary heart disease risk models developed from the US Framingham cohort (39) were found to overestimate risk in some cohorts (40,41), whereas underestimating risk in socioeconomically deprived individuals (42). A simple ‘‘recalibration’’ factor appeared to resolve the discrepancy as the relative difference was roughly constant at all levels of risk (40). A corollary to using a population-calibrated risk assessment system for deriving absolute fracture risk is that the choice of reference population becomes arbitrary. For example, we could derive fracture risk for individual A from population P1 (Fig. 2) using reference data for P1. Alternatively, we could also derive fracture risk for individual A using reference data for population P2. Recall that T-scores using population P2 reference data would be 2 SD lower than using population P2 reference data (equivalent to a four-fold increase in the densitometric relative risk term assuming a doubling in fracture risk for each SD). Providing that the calibration factor is re-scaled to compensate for this systematic difference (i.e., R0 using population P2 reference data should be one-quarter the R0 for population P1 reference data), then the risk calculations become mathematically equivalent. By extension, BMD reference data from any population could probably be used for absolute fracture risk assessment in any other population. One recent study has specifically examined the interaction of race/ethnicity with chronic disease risk factors for osteoporotic fracture (43). The populations that were studied (aboriginal and non-aboriginal Canadians) had large differences in fracture rates and chronic disease burden. Greater prevalence of diabetes, comorbidity, and substance abuse contributed to higher rates of fracture in the aboriginal population, but the relative risk of fracture for these factors was similar. In this analysis, race/ethnicity was essentially a risk factor for other risk factors (Fig. 3). As noted earlier, selection of risk factors that are causally linked to the observed ethnic differences in osteoporotic fracture rate variations provides a more direct and equitable approach. At the same time, it addresses the contribution of similar factors within an ethnically homogeneous population. For example, if BMAD is adopted as an equitable solution to Volume 9, 2006
410 the bone density-fracture paradox in Asians, then it can also benefit whites with small skeletal size (44,45). Two articles in a recent issue of the Journal of Clinical Densitometry illustrate this principle. Andersen et al. (35) found significantly lower heel BMD in Arctic Inuit compared with whites ( p ! 0.001), but whites were significantly taller and heavier. The ethnic difference in BMD disappeared when adjusted for body size. Lage at el. (46) compared DXA-derived areal and volumetric BMD measurements in 62 females with Turner’s syndrome (a genetic disorder characterized by short stature) with BMD in 102 normal females. Most of the females with Turner’s syndrome (83.8%) had a Z-score below 1 for areal BMD (of which 41.9% had Z-scores below 2.5), whereas the majority (58.1%) had a volumetric BMD Z-score above 1 (of which only 12.9% were below 2.5). Other studies have confirmed the confounding effect of size on assessment of BMD in Turner’s syndrome (47,48). Although the two populations studied were quite different, both studies gave the same message: BMD measurements that did not account for body size underestimated bone mass and potentially overestimated skeletal fragility. Similarly, where fracture risk in an ethnic group is attributable to geography, nutrition, or comorbid illness, then inclusion of these factors (either directly or indirectly) creates a model that is more transparent and applicable to the larger population, and avoids the use of race/ethnicity as a proxy variable. It has been argued that race/ethnicity should only be used when it is the only available proxy for important unmeasured variables (4). For example, several measures of socioeconomic status have been associated with BMD (49e51) and fracture risk (52e54). It would be clearly inappropriate to suggest a need for social class-specific reference data for BMD measurements.
Conclusions Race/ethnicity can be seen as both an ecosocial construct and a biomedical descriptor. When viewed as the former, issues of population-specific reference data become much less important. Others will emphasize the biological aspects of race/ethnicity. Both perspectives can be supported, and only more scientific study and dialogue will clarify their relative importance. Much more research is required to better characterize the nature of the density-fracture relationship in relation to gender, geography/environment, and race/ethnicity. Use of a common reference population enhances consistent reporting and reduces confusion, and would probably be applicable to most (if not all) populations, providing there are appropriate steps to calibrate the fracture prediction model. White reference data are the most complete and best validated as a tool for fracture prediction and treatment initiation, but this is no way implies that white reference data are ‘‘best’’ or that other reference data (including a globally pooled reference population) would not serve equally well. Where different populations share similar density-fracture relationships, this would imply the use of the common reference population (i.e., no adjustment for country or race/ethnicity). Journal of Clinical Densitometry
Leslie and Lentle Even in regions where there is strong scientific evidence to support an alteration in the normal density-fracture relationship, the use of a common reference population may still be satisfactory, providing that there is an appropriate calibration factor or an equivalent approach that directly addresses the underlying etiology for the discordance (e.g., skeletal size and weight adjustment for Asian populations). The move toward absolute fracture risk as the basis for intervention will likely neutralize much of the current controversy regarding choice of reference population, because the T-score becomes an intermediate measure on the path linking BMD to fracture risk. Providing that the fracture prediction model has been appropriately calibrated, any reference population can be used. As we celebrate our cultural history and heritage as individuals and groups, race/ethnicity presents a challenge to those involved in the field of bone densitometry. The ‘‘right’’ response goes far beyond simply addressing issues related to reference population data, because this can not resolve ethnic disparities in osteoporotic fracture rates. Race/ethnicity is often a proxy for other disease-related risk factors. As Hadler wrote (55): ‘‘After a century of presumption and assumption, modern science is finally deconstructing the construct of ‘race.’ There are genetic differences between populations with differing ancestral continents of origin. However, the genomic similarities are far more striking than the differences. Furthermore, in an outbred population such as the citizenry of the United States, ethnic and racial labeling is on the shakiest of genetic grounds. The label should be used with caution so as not to reinforce untenable stereotypes while to mask the sociocultural variables that are far more influential in terms of longevity and other health outcomes.’’ Understanding the true causal risk factors allows us to get beyond the superficial shorthand that comes with the language of race/ethnicity. Improved understanding can only enhance risk assessment and appropriate intervention.
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Journal of Clinical Densitometry
Leslie and Lentle 49. Clark EM, Ness A, Tobias JH. 2005 Social position affects bone mass in childhood through opposing actions on height and weight. J Bone Miner Res 20:2082e2089. 50. Varenna M, Binelli L, Zucchi F, Ghiringhelli D, Gallazzi M, Sinigaglia L. 1999 Prevalence of osteoporosis by educational level in a cohort of postmenopausal women. Osteoporos Int 9:236e241. 51. Wang MC, Dixon LB. 2006 Socioeconomic influences on bone health in postmenopausal women: findings from NHANES III, 1988-1994. Osteoporos Int 17:91e98. 52. Bacon WE, Hadden WC. 2000 Occurrence of hip fractures and socioeconomic position. J Aging Health 12:193e203. 53. Farahmand BY, Persson PG, Michaelsson K, Baron JA, Parker MG, Ljunghall S. 2000 Socioeconomic status, marital status and hip fracture risk: a population-based case-control study. Osteoporos Int 11:803e808. 54. Leslie WD, Derksen SA, Metge C, et al. 2005 Demographic risk factors for fracture in First Nations people. Can J Public Health 96(Suppl 1):S45eS50. 55. Hadler NM. 2004 The last well person: how to stay well despite the health-care system. Montreal: McGill-Queens University Press.
Volume 9, 2006