Biphasic fracture risk in diabetes: A population-based study

Bone 40 (2007) 1595 – 1601 www.elsevier.com/locate/bone Biphasic fracture risk in diabetes: A population-based study William D. Leslie a,⁎, Lisa M. Lix b , Heather J. Prior b , Shelley Derksen b , Colleen Metge b , John O'Neil c a Department of Medicine (C5121), University of Manitoba, 409 Tache Avenue, Winnipeg, Manitoba, Canada R2H 2A6 b Manitoba Centre for Health Policy, University of Manitoba, Winnipeg, Canada R2H 2A6 c Community Health Sciences, University of Manitoba, Winnipeg, Canada R2H 2A6 Received 24 October 2006; revised 13 February 2007; accepted 21 February 2007 Available online 1 March 2007 Abstract Diabetes is associated with increased fracture rates but the effect size, time course and modifying factors are poorly understood. This study was undertaken to assess the effect of diabetes on fracture rates and possible interactions with age, duration of diabetes and comorbidity. A retrospective, population-based matched cohort study (1984–2004) was performed using the Population Health Information System (POPULIS) for the Province of Manitoba, Canada. The study cohort consisted of 82,094 diabetic adults and 236,682 non-diabetic matched controls. Diabetes was subclassified as long term, short term, and newly diagnosed. Number of ambulatory diagnostic groups (ADGs) was an index of comorbidity. Poisson regression was used to study counts of combined hip, wrist and spine (osteoporotic) fractures (5691 with diabetes and 16,457 without diabetes) and hip fractures (1901 with diabetes and 5224 without diabetes). Independent effects of longer duration of diabetes (p-for-trend < 0.0001) and number of ADGs (p-for-trend < 0.0001) were observed on fracture rates. Newly diagnosed diabetes showed a reduction in osteoporotic fractures (rate ratio [RR] 0.91 [95% CI, 0.86–0.95]) and hip fractures (RR 0.83 [0.75–0.92]). Long-term diabetes showed an increase in osteoporotic fractures (RR 1.15 [CI, 1.09–1.22]) and hip fractures (RR 1.40 [1.28–1.53]). We conclude that long-term diabetes is associated with increased fracture risk, whereas newly diagnosed diabetes shows a reduction in fractures. It is hypothesized that the opposing effects of overweight/obesity and diabetes-related complications contribute to the observed biphasic fracture risk, though causality cannot be proven from this observational study. © 2007 Elsevier Inc. All rights reserved. Keywords: Epidemiology; Population studies; Osteoporosis; Fractures; Diabetes Introduction Osteoporotic fractures are highly prevalent in developed countries. About 40% of women experience an osteoporosisrelated fracture in the course of their lifetime with men at approximately one-third to one-half the risk of women [1,2]. Low-trauma fractures, the clinical expression of osteoporosis, increase dramatically with age [3]. Since many chronic health problems become more prevalent with age, osteoporosis fre- ⁎ Corresponding author. Fax: +1 204 237 2007. E-mail addresses: [email protected] (W.D. Leslie), [email protected] (L.M. Lix), [email protected] (H.J. Prior), [email protected] (S. Derksen), [email protected] (C. Metge), [email protected] (J. O'Neil). 8756-3282/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.bone.2007.02.021 quently occurs in conjunction with other disorders which may interact (positively or negatively) in terms of fracture risk. Diabetes is much more frequent in the elderly [4]. Furthermore, there is a global increase in the prevalence of diabetes and obesity [5], just as there is for osteoporosis [4]. Initially it was felt that type 2 diabetes might be protected against fractures due to the effect of overweight/obesity, a risk factor for type 2 diabetes whereas underweight is a risk factor for low bone density and fractures [6]. Subsequent studies have shown that bone density is increased in type 2 diabetes, but fracture risk is paradoxically increased [7–9]. Diabetic complications leading to falls and/or compromised bone quality may underlie this effect. Duration of diabetes is therefore likely to be an important modifier of this fracture risk, though not all studies have confirmed an effect of duration [10]. Age and chronic comorbidities could modify the fracture 1596 W.D. Leslie et al. / Bone 40 (2007) 1595–1601 risk associated with diabetes, but studies to date have not assessed these interactions. Materials and methods A retrospective, matched-cohort study was performed using the Population Health Information System (POPULIS) data repository at the Manitoba Centre for Health Policy (MCHP) [11]. Manitoba Health provides comprehensive health care coverage for residents of Manitoba, Canada, and maintains computerized databases of health services that include demographics, date and type of service, and diagnoses from the International Classification of Disease-9Clinical Modification (ICD-9-CM). An encrypted personal identifier allows for linkage across datasets and creation of person-specific longitudinal records of health service utilization. The study was approved by the Research Ethics Board for the University of Manitoba and Health Information Privacy Committee of Manitoba Health. The study cohort included all diabetic adults (aged 20 years or older as of December 31, 1993) who were continuously residents in the province from 1984 to the end of 1993. Individuals who migrated into, or out of the province, or who died during the pre-inception period, were excluded. The presence of diabetes prior to study inception (January 1, 1994) was based upon a validated definition: two physician office visits or a single hospitalization with a diagnosis of diabetes (ICD-9-CM code 250) in a 3-year period [4]. Records for preceding years were used to subclassify diabetes as long term (diabetic more than 5 years prior to inception) and short term (diabetic less than 5 years prior to inception) [12]. Individuals not meeting the diabetes definition at inception but who did meet this definition during the follow-up study period were categorized as newly diabetic. In total, 82,094 men and women met one of these mutually exclusive diabetes categories. Diabetes was classified as new onset in 42,874 subjects (52.2%), short term in 16,081 (19.6%) and long term in 23,139 (28.2%). Up to three controls (non-diabetic at inception as well as throughout the follow-up study period) were randomly matched to each diabetic subject. Controls were matched by birth year, gender, and area of residence within the province. First Nations Aboriginal ethnicity (also referred to as Natives or “Indians”) identified from government files was an additional matching variable since Canadian Aboriginals have higher rates of fracture and diabetes than nonAboriginals [13,14]. No match could be found for 708 subjects and these were excluded from analysis. The final cohort therefore consisted of 82,094 diabetic adults and 236,682 non-diabetic controls. Fractures that occurred from study inception (January 1, 1994) until the end of the follow-up period (March 31, 2004 unless the individual died or left the province) were identified. Vertebral fractures (ICD-9-CM code 805), wrist fractures (ICD-9-CM code 813) and hip fractures (ICD-9-CM code 820–821) were taken to be indices of osteoporotic fractures [13]. Hip fractures had to be accompanied by a physician claim for a site-specific fracture reduction or fixation. Hip fractures were studied as a specific subgroup due to the clinical importance of these fractures in terms of subsequent morbidity and mortality. Area of residence in the province was defined at study inception and income quintiles were defined using average household income from the 1996 Statistics Canada Census [15]. The Johns Hopkins Ambulatory Care Group system was used to develop an index of population comorbidity [16]. Ambulatory diagnostic groups (ADGs) represent 32 comorbidity clusters of every ICD-9-CM diagnostic code. The number of ADGs was categorized as none (reference category), 1–2, 3–5 and 6 or more. Crude (unadjusted) fracture rates were calculated per thousand person-years. Analysis of crude fracture rates could be confounded by the fact that diabetes prevalence, diabetes duration and multiple ADGs increase in older individuals. Therefore, multivariable models were constructed to assess the independent effects of these variables as well as their possible interactions. Poisson regression was used to model counts of the number of fractures in population strata as a function of the selected demographic, geographic, socioeconomic, and chronic disease variables. The number of person-years at risk in each stratum was the offset variable in the Poisson models. The base regression model included ethnicity, age (10-year groups unless otherwise specified), gender, area of residence, income quintile and the interactions of age*gender and residential area*income quintile. Matching variables were included in the models since they produced a statistically significant improvement in model fit. Full reg- ression models included all of the variables in the base model in addition to chronic disease variables for diabetes diagnosis (Model 1), number of ADGs (Model 2), and their combination (Model 3). Diabetes diagnosis, number of ADGs, and age were included as main effects and two-way interactions [17]. Rate ratios (RR) and 95% confidence intervals (CI) are reported. The difference between the base and full models was evaluated using a likelihood ratio test [18]. To achieve satisfactory model fit, ages below 60 years were combined. It was also necessary to combine income quintiles 1–2 (lowest income quintiles) and 3–5 (highest income quintiles; reference category). All regression analyses were performed using the GENMOD procedure of SAS Release 8.02 (SAS Institute Inc., Cary, NC). Results The composition and demographics of the cohorts are summarized in Table 1. The non-diabetic and diabetic groups were well-matched in terms of age, gender, area of residence and income level. Since duration of diabetes correlates with age, those with long-term diabetes tended to be slightly older than short-term or newly diabetic subjects. The distribution in ADG category was consistent with higher comorbidity in the diabetic cohort (p < 0.0001) with a significant difference in those with 6 or more ADGs (22.7% vs. 15.2%, p < 0.0001). The diabetic cohort provided 715,148 person-years of followup with 2,071,417 person-years of follow-up in the non-diabetic controls (Table 1). There were sufficient numbers of osteoporotic fractures (combined hip, wrist and spine n = 22,148 [16,457 in those without diabetes and 5691 in those with diabetes]) and isolated hip fractures (n = 7125 [5224 in those without diabetes and 1901 in those with diabetes]) for analyses of main effects and interactions. The crude osteoporotic fracture rate in the non-diabetic controls was 8.2 per 1000 person-years (95% confidence interval [CI] 8.0–8.3) which was identical to the rate for those with diabetes (8.2, 95% CI 7.9–8.4, p > 0.2). Osteoporotic fracture rates increased with age in both cohorts (Table 2). Osteoporotic fracture rates were significantly greater in the diabetes cohort than among non-diabetic controls prior to age 60 years, with an excess in osteoporotic fractures after age 80 years in controls without diabetes compared to subjects with diabetes (p = 0.0019). Overall hip fractures rates showed a borderline difference (diabetes 2.5 per 1000 person-years [95% CI 2.4– 2.6] vs. controls 2.7 [2.5–2.8], p = 0.06). Hip fracture rates were significantly greater in diabetic subjects than controls prior to age 80 years, but roughly equal in those age 80 years and older. Within the diabetic cohort there was a general pattern of higher age-specific fracture rates related to longer disease duration (Fig. 1) and this was statistically significant (osteoporotic fractures p-for-trend = 0.026, hip fractures p-for-trend = 0.025). Crude fracture rates showed a strong relationship with comorbidity as represented by the number of ADGs. Individuals in the lowest ADG category experienced 4.9 osteoporotic fractures per 1000 person-years (95% CI 4.7–5.1) as compared with 6.5 (95% CI 6.3–6.6), 9.0 (8.8–9.2) and 13.5 (13.2–13.9) with progressively higher ADG categories. Hip fractures showed the same association, increasing from 1.3 per 1000 person-years (95% CI 1.2–1.4) in the lowest ADG category to 4.7 (95% CI 4.5–4.9) in the highest ADG category. These patterns 1597 W.D. Leslie et al. / Bone 40 (2007) 1595–1601 Table 1 Cohort demographics and number of fracture endpoints observed (percentages in parentheses) Controls N = 236,682 Diabetes — all N = 82,094 Diabetes — new N = 42,874 Diabetes — short term N = 16,081 Diabetes — long term N = 23,139 58.0 ± 16.1 118,623 (50.1) 17,038 (7.2) 57.9 ± 15.4 40,909 (49.8) 8776 (10.7) 54.0 ± 15.3 22,013 (51.3) 4865 (11.3) 60.2 ± 15.6 7813 (48.6) 1702 (10.6) 63.4 ± 15.2 11,083 (47.9) 2209 (9.5) 13,769 (5.8) 91,753 (38.8) 131,160 (55.4) 5753 (7.0) 31,949 (38.9) 44,392 (54.1) 3230 (7.5) 16,665 (38.9) 22,979 (53.6) 1095 (6.8) 6169 (38.4) 8817 (54.8) 1428 (6.2) 9115 (39.4) 12,596 (54.4) 49,941 (21.1) 51,510 (21.8) 49,814 (21.0) 42,171 (17.8) 39,462 (16.7) 3784 (1.6) 20,832 (25.4) 18,036 (22.0) 16,762 (20.4) 13,502 (16.4) 11,703 (14.3) 1259 (1.5) 10,406 (24.3) 9316 (21.7) 8766 (20.5) 7452 (17.4) 6601 (15.4) 333 (0.8) 4201 (26.1) 3456 (21.5) 3374 (21.0) 2479 (15.4) 2247 (14) 324 (2.0) 6225 (26.9) 5264 (22.8) 4622 (20.0) 3571 (15.4) 2855 (12.3) 602 (2.6) Number of ambulatory diagnostic groups (ADGs): None 39,733 (16.8) 1–2 81,308 (34.4) 3–5 79,772 (33.7) 6 or more 35,869 (15.2) Follow-up (person-years) 2,071,417 7488 (9.1) 24,787 (30.2) 31,169 (38.0) 18,650 (22.7) 715,148 5893 (13.7) 14,224 (33.2) 15,316 (35.7) 7441 (17.4) 410,703 584 (3.6) 4525 (28.1) 6558 (40.8) 4414 (27.5) 133,140 1011 (4.4) 6038 (26.1) 9295 (40.2) 6795 (29.4) 171,304 Numbers of fractures: Hip Spine Wrist 1686 (2.1) 1287 (1.6) 2718 (3.3) 551 (1.3) 599 (1.4) 1431 (3.3) 379 (2.4) 242 (1.5) 535 (3.3) 756 (3.3) 446 (1.9) 752 (3.2) Age (years), mean ± SD Men Aboriginal status Residence: Rural north Rural south Urban Income quintile: Quintile 1 (lowest) Quintile 2 Quintile 3 Quintile 4 Quintile 5 (highest) Not available 4570 (1.9) 3519 (1.5) 8368 (3.5) a progressive increase in osteoporotic and hip risk with more diagnoses (p-for-trend < 0.0001). No statistically significant interaction was identified between diabetes category and comorbidity (likelihood ratio test p > 0.2). When both variables were included in the regression (Model 3), there was no appreciable weakening in their contribution to fracture prediction when assessed separately. Age showed a statistically significant interaction with diabetes category and number of ADGs. Models that included terms for age*diabetes and age*ADG showed a corresponding improvement in model fit (likelihood ratio test p < 0.0001). There was a gradient of increasing osteoporotic fracture risk with longer duration of diabetes across the three diabetes categories which was statistically significant (p-for-trend < 0.05) for all age groups except individuals age 80 and older (p-for-trend > 0.2). Fig. 3 demonstrates the age interaction with wereevident within every age stratum (Fig. 2) and were confirmed in the linear trend analysis (osteoporotic and hip fractures p-for-trend < 0.0001). The regression analyses confirmed the importance of diabetes duration on the occurrence of osteoporotic and hip fractures (Table 3). As compared with the non-diabetic reference group (Model 1), newly diagnosed diabetes showed a significantly reduced risk of osteoporotic fractures (rate ratio [RR] 0.91 [95% CI, 0.86–0.95]) and hip fractures (RR 0.83 [0.75–0.92]). Long-term diabetes showed an increase in both osteoporotic fractures (RR 1.15 [CI, 1.09–1.22]) and hip fractures (RR 1.40 [1.28–1.53]). Short-term diabetes was neutral in terms of fracture of fracture risk. The test for linear trend across these three diabetes categories was significant for osteoporotic and hip fractures (p-for-trend < 0.0001). Comorbidity, as measured by the number of ADGs (Model 2), showed Table 2 Crude rates per 1000 person-years for osteoporotic fractures (spine, hip or wrist) and hip fractures in non-diabetics controls and diabetic subjects (95% confidence intervals are given in parentheses) Age <50 years 50–59 years 60–69 years 70–79 years 80 years and over Overall Osteoporotic fractures Hip fractures Controls N = 236,682 Diabetes N = 82,094 p Controls N = 236,682 Diabetes N = 82,094 p 3.4 (3.3–3.5) 4.7 (4.5–4.9) 7.8 (7.5–8.0) 15.3 (14.9–15.8) 33.1 (32.0–34.2) 8.2 (8.0–8.3) 4.1 (3.9–4.4) 5.3 (4.9–5.6) 8.0 (7.6–8.5) 15.1 (14.4–15.9) 29.4 (27.6–31.3) 8.2 (7.9–8.4) < 0.00001 0.01462 > 0.2 > 0.2 0.00189 > 0.2 0.1 (0.1–0.1) 0.4 (0.4–0.5) 1.6 (1.5–1.7) 6.2 (6.0–6.5) 19.2 (18.4–20.1) 2.5 (2.4–2.6) 0.3 (0.2–0.4) 0.7 (0.6–0.9) 2.1 (1.9–2.3) 6.8 (6.3–7.3) 17.9 (16.6–19.4) 2.7 (2.5–2.8) <0.00001 0.00003 0.00005 0.04595 0.12445 0.05764 1598 W.D. Leslie et al. / Bone 40 (2007) 1595–1601 time that a biphasic effect of diabetes on fracture rates has been observed. An index of comorbidity was also associated with increased fracture risk, but this was independent of the effect of diabetes. The effect of age on osteoporotic fracture rates is complex, since younger diabetics appeared have higher rates than controls whereas older diabetics had reduced rates of fractures compared with controls. These opposing effects cancelled each other out so that the overall rates were the same. Although some studies have found higher fracture rates in diabetic patients [7–9], a population-based study comparing 986 community diabetics (median age at diagnosis 61 years) with matched non-diabetic controls found no differences in fracture rates [19]. The explanation the age interaction that we observed is unclear. Shorter life expectancy in older diabetics could be one possible explanation, but Cox proportional models of time to first fracture (which adjust for differential survival) gave similar results to the Poisson regression models (data not shown). It has been projected that the number of people with diabetes worldwide will double between 2000 and 2030 even if levels of obesity remain constant [5]. Although most of this increase will be in older adults, it is important to note that children and Fig. 1. Age-specific crude fracture rates (per 1000 person-years) in the diabetic cohort according to duration of diabetes. (A) Osteoporotic fractures (combined spine, hip and wrist). (B) Hip fractures only. 95% Confidence intervals are shown. long-term diabetes less than age 50 showing increased fracture risk (relative to controls) while newly diagnosed and short-term diabetes showed fracture rates equal to controls. In contrast, newly diagnosed diabetes age 80 and older showed reduced fracture risk (relative to controls) while long-term term diabetes had fracture rates equal to controls. A similar pattern was evident for hip fractures (due to the small number of fractures some age strata were combined). The test for linear trend across the three diabetes categories was statistically significant for those less than 60 years and 60–79 years age groups (p-fortrend < 0.0001), and borderline in those age 80 and older (p-fortrend = 0.08). The number of ADGs showed a consistent gradient of increasing osteoporotic and hip fracture risk (pfor-trend < 0.0001 for all age groups). Discussion We found that longer diabetes duration correlated with higher osteoporotic and hip fracture risk, consistent with the hypothesis that diabetes mediates its effect fractures through its associated complications. Newly diagnosed diabetic subjects were actually at slightly reduced fracture risk. This is the first Fig. 2. Age-specific crude fracture rates (per 1000 person-years) according to level of comorbidity (number of ambulatory diagnostic groups [ADGs]). (A) Osteoporotic fractures (combined spine, hip and wrist). (B) Hip fractures only. 95% Confidence intervals are shown. W.D. Leslie et al. / Bone 40 (2007) 1595–1601 Table 3 Fracture rate ratios from Poisson regression models (95% confidence intervals are given in parentheses) Variables in model a Hip fractures Hip fractures Reference 0.91 (0.86–0.95) 1.00 (0.93–1.07) 1.15 (1.09–1.22) p < 0.0001 Reference 0.83 (0.75–0.92) 1.13 (1.00–1.28) 1.40 (1.28–1.53) p < 0.0001 Model 2: comorbidity Number of ambulatory diagnostic groups (ADGs): None Reference 1–2 1.13 (1.05–1.21) 3–5 1.33 (1.24–1.42) 6 or more 1.72 (1.60–1.85) Linear trend for ADG categories p < 0.0001 Reference 1.07 (0.95–1.20) 1.20 (1.07–1.34) 1.53 (1.36–1.71) p < 0.0001 Model 1: diabetes Diabetes diagnosis Controls Diabetes — new Diabetes — short term (<5 years) Diabetes — long term (>5 years) Linear trend for diabetes categories Model 3: diabetes and comorbidity Diabetes diagnosis: Controls Diabetes — new Diabetes — short term (<5 years) Diabetes — long term (>5 years) Linear trend for diabetes categories Number of ambulatory diagnostic groups (ADGs): None 1–2 3–5 6 or more Linear trend for ADG categories a Reference 0.89 (0.85–0.94) 0.94 (0.88–1.01) 1.09 (1.05–1.15) p = 0.0001 Reference 0.82 (0.74–0.91) 1.10 (0.97–1.24) 1.36 (1.24–1.49) p < 0.0001 Reference 1.13 (1.06–1.20) 1.32 (1.24–1.40) 1.67 (1.57–1.78) p < 0.0001 Reference 1.05 (0.93–1.18) 1.16 (1.04–1.30) 1.48 (1.32–1.66) p < 0.0001 Adjusted for age, sex, income quintile, area of residence and ethnicity. adolescents are not spared [20,21]. Since young persons with diabetes will have a lifetime during which to develop diabetic complications, they may be particularly vulnerable to the adverse skeletal effects. Therefore, our finding that younger persons with diabetes have the highest risk ratios (up to fourfold for hip fractures in long-term diabetes) may have important clinical implications. Our study provides a population-based perspective of how diabetes of all types and durations affects fracture risk, but is limited by our inability to differentiate type 1 from type 2 diabetes. The pathogenetic mechanism underlying osteoporosis may differ according to diabetes type [22], though the largest study to date found similar relative risk for fracture for type 1 and type 2 diabetes. In a large case-control study from Denmark, both types of diabetes were associated with similarly increased risk 1599 for fracture (OR 1.3, 95% CI 1.2–1.5 for type 1; OR 1.2, 95% CI 1.1–1.3 for type 2) and hip fractures (OR 1.7, 95% CI 1.3–22 vs. OR 1.4, 95% CI 1.2–1.6) [23]. The population-based cohort from Tromso, Norway, noted increased non-vertebral and hip fracture risk regardless of diabetes type but no effect of diabetes duration [10]. Other studies report more hip fractures in type 1 diabetes (RR 6.9, 95% CI 2.2–21.6) than in type 2 diabetes (RR 1.8, 95% CI 1.1–2.9) [24]. In this latter study, type 2 diabetes showed no increased risk of hip fractures if the duration of disease was less than 5 years. Relative risk for hip fracture in type 1 diabetic men and women almost eightfold and 10-fold higher than matched controls have been reported [25]. It is possible that the declining fraction of type 1 diabetes in older populations contributes to the age interaction observed in our study. The Rotterdam study assessed BMD and fractures in 792 type 2 diabetes in relation to glucose tolerance tests [26,27]. Diabetic subjects had higher bone mineral density with increased non-vertebral fracture risk (hazard ratio [HR] 1.33, 95% CI 1.00–1.77) after adjustment for age, gender, BMI, selected clinical risk factors, and femoral bone density. The Health, Aging and Bone Composition (ABC) Study included 2979 men and women of mixed ethnicity age 70–79 years of age. Type 2 diabetes was present in 566 (19%) and predicted higher bone density independent of body composition and fasting insulin [28]. Diabetic subjects had similar unadjusted fracture rates as non-diabetic subjects (11.7 per 1000 person-years vs. 12.4 per 1000 person-years). No significant effect of diabetes on fracture risk was seen in regression models that adjusted for demographic variables only (RR for diabetes 1.23, 95% CI 0.82– 1.86) but those with diabetes were at higher fracture risk after adjustment for higher femoral bone density (RR 1.71, 95% CI 1.11–2.61) [9]. This study recently reported that thiazolidinediones, which activate the peroxisome proliferator-activated receptor (PPAR)-gamma, may cause bone loss in older women and suggests another potential mechanism contributing to higher fracture risk [29]. One previous study has examined women in whom diabetes developed during follow-up [7], comparable to our newly diagnosed diabetes category. Hip fracture risk (RR 1.60, 95% CI 1.14–2.25) was increased which is contrary to our finding. Diabetes status, risk factors and hip fracture were ascertained by mailed questionnaire and it is not possible to exclude respondent bias. The effect of overweight/obesity and fracture risk deserves comment. Insulin resistance is directly correlated with higher bone density in men and women, possibly mediated through the effects of body weight rather than hyperinsulinemia [30], though the mechanism through which weight affects bone turnover, bone density and fractures is not well defined and may involve currently unknown hormones or growth factors [31]. BMI 25 kg/m2 confers a twofold reduction in hip fracture compared to a BMI 20 kg/m2 with a 17% additional reduction in hip fracture risk with a BMI 30 kg/m2 [6]. Greater weight is a risk factor for type 2 diabetes and could contribute to the apparent protection from osteoporotic and hip fractures that we observed in newly diagnosed diabetes. Unfortunately, weight cannot be assessed in our data sources. 1600 W.D. Leslie et al. / Bone 40 (2007) 1595–1601 Fig. 3. Interaction between age and (A) diabetes category or (B) comorbidity (number of ambulatory diagnostic groups [ADGs]). Rate ratios are from Poisson regression models that included diabetes category (reference category: non-diabetic controls), number of ADGs (reference category: none), age⁎diabetes category and age⁎number of ADGs. 95% Confidence intervals are shown. Our study of diabetes and fractures is the largest to date thanks to a comprehensive administrative health data repository. Fracture ascertainment from administrative health data is not without limitations, however. Fractures that produce few symptoms may not lead to a physician interaction. We are also unable to determine whether increased fracture risk relates to reduced bone density, increased risk for falls, or other unidentified factors. Risk estimates were not adjusted for weight, insulin or medication use, diabetic control, or other clinical risk factors that affect fractures [32]. Our diabetes definition has been validated in terms of prevalence but is a relatively crude measure of disease duration [4]. The inclusion of individuals first diagnosed with diabetes after inception in the diabetic cohort could be questioned. Type 2 diabetes is characterized by a long asymptomatic period. Subjects in whom diabetes was diagnosed after cohort inception probably had glucose dysregulation and risk factors predisposing to diabetes for some time prior to diagnosis. Therefore, it is more appropriate to include them with the diabetic group than with the non-diabetic controls. In summary, long-term diabetes is associated with increased fracture risk whereas newly diagnosed diabetes shows a reduction in fracture risk, though this relationship is modified by an interaction with age. Our findings are consistent with the view that disease-related complications are primarily responsible for the excess fracture risk observed in diabetes. It is hypothesized that the opposing effect of overweight/obesity in newly diagnosed diabetes contributes to the observed biphasic fracture risk, though causality cannot be proven from this observational study. Acknowledgments Supported by a research grant from the Canadian Institutes for Health Research. The authors are indebted to Manitoba Health for providing the data used in this study, to the First Nations and Inuit Health Branch and Indian and Northern Affairs Canada for permission to use the Status Verification System, and to the Health Information Research Committee of the Assembly of Manitoba Chiefs for actively supporting this work. The results and conclusions are those of the authors, and no official endorsement by Manitoba Health is intended or should be inferred. Sources of support: A research grant from the Canadian Institutes for Health Research. References [1] Kanis JA, Johnell O, Oden A, Sembo I, Redlund-Johnell I, Dawson A, et al. 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