Osteoporosis International

, Volume 20, Issue 4, pp 557–565 | Cite as

Sister’s fracture history may be associated with perimenopausal bone fragility and modifies the predictability of fracture risk

  • J. Sirola
  • K. Salovaara
  • M. Tuppurainen
  • J. S. Jurvelin
  • E. Alhava
  • H. Kröger
Original Article

Abstract

Summary

The present study investigated the effects of first degree relatives’ fractures on fracture incidence after the menopause. Sister’s, but not other relatives’, wrist or hip fracture history was associated with increased risk of fragility fractures after the menopause. This suggests genetic predisposition to bone fragility among postmenopausal women.

Objective

The aim of the present study was to investigate the association between first degree relatives’ fractures and perimenopausal bone fragility.

Materials and methods

The study sample of 971 perimenopausal women was extracted from randomly selected Kuopio Osteoporosis Risk Factor and Prevention cohort and measured with dual X-ray absorptiometry in femoral neck (FN) in baseline (1989–1991), in 5 years (1994–97), and in 10 years (1999–2001). All low-trauma energy fractures during the 10-year follow-up were recorded based on self-reports and validated from medical records. First degree relatives’ history of life-time hip and wrist fractures (exact classification or trauma energy not specified) was questioned by postal inquiries.

Results

There was a significant correlation between fathers’ vs. brothers’ and mothers’ vs. sisters’ fractures (p < 0.01 in Pearson bivariate correlations). Sister’s, but not mother’s, father’s, or brother’s wrist and hip fractures were associated with significantly lowered 10-year fragility fracture-free survival rate (HR = 0.56, p = 0.006). Sisters’ or other relatives’ fractures were not associated with FN bone loss rate or bone mineral density (BMD) in the follow-up measurements (p = NS in ANCOVA). The predictive power of BMD for fragility fractures differed according to sisters’ fracture history: Baseline FN T score predicted fracture-free survival only among women without sisters’ fracture history (HR 0.62, p < 0.001 vs. women with sisters’ fracture in Cox regression).

Conclusions

In conclusion, sisters’ fracture history is associated with 10-year fracture-free survival in perimenopausal women but not with BMD or its changes. Predictability of fragility fracture risk with BMD may depend on sister’s fracture history. This may indirectly suggest genetic predisposition to bone fragility independently of BMD.

Keywords

Fracture risk Genetics Osteoporosis Perimenopause Population-based study 

Introduction

Osteoporotic fractures result in significant morbidity and mortality [1, 2]. The risk of fractures is greater among women with high bone loss rate, although differences in bone mineral density (BMD) and its changes explain only 20% of the fractures among the elderly [3]. Overall, the individual fracture risk is a sum of falling propensity and bone fragility and accordingly may be modified by genetic predisposition and several external environmental risk factors.

Recent studies have investigated the risk factors for osteoporotic fractures [4, 5, 6, 7, 8, 9]. The most important environmental risk factors for low-trauma energy postmenopausal fractures are low BMD, previous fracture history, body composition, menopausal transition, excessive alcohol consumption, and smoking [4, 5, 6, 7, 8]. In addition, family history of fractures have been claimed to be associated with increased perimenopausal risk of bone fragility in number of epidemiological studies [9, 10, 11, 12, 13, 14, 15].

Osteoporotic fractures have been suggested to partly be a genetically controlled disease [16, 17, 18, 19, 20]. However, the genetic correlation between BMD and osteoporotic fractures may be low, and accordingly, heritability of low-trauma energy fracture susceptibility has been suggested to be independent of BMD [16, 17]. In addition, it has been suggested that genetic predisposition to fractures may be modified by variety of environmental factors [17]. Although the prediction of perimenopausal fractures is generally achieved by bone density measurements, a variety of risk factors and, e.g., muscle strength measures may be used independently of BMD for evaluation of individual bone fragility [21, 22].

The aim of the present population-based study was to investigate the association between first degree relatives’ history of fractures and perimenopausal risk of fragility fractures and BMD. In addition, the ability of BMD to predict fragility fractures in the light of the relatives’ fracture history was specifically explored.

Materials and methods

Study population

The study population was formed based on the prospective Kuopio Osteoporosis Risk Factor and Prevention (OSTPRE) study cohort. The OSTPRE cohort was established in 1989 by selecting all women born in 1932–1941 and residents of Kuopio Province, Finland (n = 14,220) [23]. The baseline postal inquiry, including questions, e.g., about health disorders, medication, use of hormone replacement therapy (HRT), gynecological history, nutritional habits, calcium intake, physical activity, alcohol consumption, smoking habits, and anthropometric information, was sent to these women at baseline in 1989. The 5- (in 1994–97) and 10-year (1999–2001) follow-up questionnaires were sent to the 13,100 women who responded at baseline with response rates of 11,954 (5 years) and 11,537 (10 years) women. The present study was performed retrospectively by stratifying the study group of interest, described below in detail.

Of the 13,100 respondents in 1989, 11,055 (84.4%) were willing to undergo dual energy X-ray absorptiometry (DXA) densitometry. A sub-sample from this cohort was selected for central bone density measurements since all the population was not designed to be resourced for DXA measurement protocols. Accordingly, a densitometry sample of 3,686 women (33.3%) was selected for the measurements, of which 3,222 (87.4%) women were actually willing to undergo baseline densitometry. Of these, the random population-based sample consisted of 2,025 women, and the remaining 1,197 women formed a non-random part, which was stratified for other study purposes or were labeled to have a high-risk profile (i.e., experienced menopause within 2 years, had certain diseases/medications affecting bone, had multiple behavioral risk factors, were selected for a HRT + vitamin D trial, or were included in additional rheumatoid arthritis sample [24, 25]). This selection was also partly done for ethical reasons in the case of high-risk subjects. In all, 1,873 women of the random part underwent the 5- and 10-year bone density measurements in due course. Serial valid measurements for lumbar spine and neck of femur were recorded for 1,438 women in both baseline and the follow-up measurements. Accordingly, severe bone deformities, including spondyloarthritis (also osteophytes), scoliosis, severe compression fractures, and prostheses among other inaccuracies, were excluded by a systematic manual review of densitometry reprints by the study group physicians.

Hysterectomized women (for whom it was impossible to define menopausal status) and premenopausally bilaterally ovariectomized women (n = 467) were additionally excluded. All remaining women were postmenopausal in baseline or have undergone menopausal transition during the 10-year follow-up period, i.e., they were perimenopausal. The beginning of menopause was defined in this study as 12-month amenorrhea [26]. The beginning of amenorrhea was based on self-reports about the last natural periods in the inquiries, and no hormonal samples were collected. Accordingly, the final study population consisted of a random sample of 971 naturally postmenopausal women aged 48.0 to 59.5 years (mean 53.3 years) at baseline, 53.2 to 65.6 years (mean 59.1 years) at 5-year follow-up densitometry, and 57.1 to 70.2 years at the end of the 10-year follow-up. The duration of follow-up time among these women varied from 9.4 to 11.6 (mean 10.3) years (baseline to 10-year follow-up).

The selection process of the present study population has been outlined in Table 1.
Table 1

Selection of the sample for the present study from the OSTPRE cohort

 

Selection process

14,220

Target population: women aged 47–56 at baseline (1989) in Kuopio, Finland

13,100

Women who responded to baseline postal inquiry in 1989

11,055

Women willing to undergo bone densitometry at baseline

3,222

Stratified densitometry sample who were resourced and underwent baseline DXA

2,025

Random part who actually underwent baseline densitometry 1989–1991 (non-random part n = 1,197)a

1,873

Random part who actually underwent 5- and 10-year densitometries in 1994–1997 and 1999–2001

1,438

Serial valid densitometry at all measurement points (no artifacts/errors)

971

No premenopausal hysterectomy or ovariectomy (i.e., surgical menopause), final study population

aExperienced menopause within 2 years, had certain diseases/medications affecting bone, had multiple behavioral risk factors, were selected for HRT + vitamin D trial, or included in additional rheumatoid arthritis sample [24, 25]

Fractures

Fractures during the 10-year follow-up period (1989–2005) were recorded based on questions (in baseline, 5- and 10-year follow-up postal questionnaires) on whether the respondent had suffered a low-trauma energy fracture during the follow-up and, if so, the type, mechanism, circumstances, and treatment of the fracture. All self-reported fractures were validated by crosschecking radiological reports from medical records by study group physicians. However, rib fractures were accepted without radiological evidence if the clinical diagnosis in the medical records was clearly and uniformly a rib fracture. For the present study, only low-trauma energy fractures were accepted. The false-positive rate in self-reported fractures was 16.5% and the false-negative rate was 21.6%, respectively [27].

History of any type of fractures of the first degree relatives were inquired by questions “Have your mother/father/brother or sister suffered a fracture of wrist or hip or both?” and separately with regards each respective relative. In addition, the age of the sister and/or brother at the time of the fracture was inquired. The study population was stratified into two groups (relative’s fracture/no relative’s fracture) according to the questions in the 10-year inquiry. Accordingly, the relative’s fracture was considered in the view of genetic predisposition rather than an a priori risk factor. The exact types/classification of relatives’ fractures or trauma energy were not specifically questioned, and accordingly, we were not able to consider this issue in the present study.

Other variables

The use of HRT (used as an adjustment variable) was calculated based on the self-reported use of estrogen containing tablets and plasters during the follow-up taken for menopausal symptoms. The validation of self-reported use of HRT with national medical prescription records has shown good correlation (97.8% of HRT users were true users) and was described in detail previously [28]. The amount of ingestion of nutritional calcium (used as an adjustment variable) was calculated based on food diaries and reported in milligram per day. The food diary proved a reasonable correlation of self-reported ingestion of milk products (r = 0.50). This has been described in detail previously [29]. Grip strength, measured with pneumatic hand-held dynamometer (Martin Vigorimeter, Germany), was used as an adjustment variable and taken to be the mean of three successive measurements. The grip strength measurement protocol of the present study has been described in detail previously [21, 22]

Bone mass measurements

The DXA measurements of ap spine (L2–L4) and femoral neck were carried out using the same Lunar DPX scanner in both baseline and 5-year measurements with the imaging and analysis protocols provided by the manufacturer (Lunar Co, Madison, WI, USA) and described earlier [24]. The measurements were carried out in Kuopio University Hospital by specially trained nurses. Quality standards were tested on daily basis. The short-term reproducibility of this method has been shown to be 0.9% for lumbar spine and 1.5% for femoral neck BMD measurements [30]. The long-term reproducibility of the DXA instrument for BMD during the study period, as determined by regular phantom measurements, was 0.4% [25]. For the purposes of the present study, normal BMD, osteopenia, and osteoporosis were defined according to WHO criteria with femoral neck T score.

The Lunar DPX scanner was changed to DPX-IQ between 5- and 10-year densitometry (5/99). To reveal possible differences in the BMD results of these scanners, 90 women [age 60 + 8 years, body height 160.0 + 5.6 cm, and weight 69.4 + 12.8 kg (Mean ± SD)] were scanned on the same day with both instruments. In both femoral neck and lumbar spine BMD, a high linear correlation (rspine = 0.990; rneck = 0.974) was found between these devices, and the best-fit first order polynomial functions were calculated and used to correct results from DPX-IQ. This has been described in more detail previously [21].

Statistical methods

Statistical analyses were carried out using the Statistical Package for Social Sciences (SPSS ver. 15, SPSS Inc., Chicago, IL, USA) for Windows. The femoral neck BMD values were converted into T scores and treated as a continuous variable. Kaplan–Meier curves were used to evaluate the fracture-free survival rate and Cox proportional hazard model to receive the corresponding hazard ratios and statistical differences.

Adjustment for age, baseline weight, baseline height, use of HRT during the follow-up, duration of menopause, grip strength, and bone-affecting diseases/medication (yes/no) was used when appropriate. The selection of bone-affecting diseases/medication (used in adjustments) has been described previously by Kröger et al. [24]. Diseases were renal disease, liver disease, insulin-dependent diabetes, malignancies, rheumatoid arthritis, endocrine abnormalities (parathyroid/thyroid glands, adrenals), malabsorption (including lactose malabsorption), total/partial gastrectomy, postovariectomy status, premenopausal amenorrhea, alcoholism, and long-term immobilization. Medication included corticosteroids, diuretics, cytotoxic drugs, anticonvulsive drugs, anabolic steroids, calcitonin, bisphosphonates, and vitamin D. The percentage of women with bone-affecting diseases did not differ significantly between the study population and total population samples (39% vs. 45%, respectively; p > 0.100).

Results

The selection process of the present study population has been outlined in Table 1.

Table 2 shows the baseline characteristic of the study population. The most common relative’s fractures were mother’s (17.7%) and sister’s fractures (9.2%). Table 3 describes the Pearson correlation coefficients of fractures between the first degree relatives. Accordingly, the fractures seemed to be paired according to sex, i.e., in mother–sister and father–brother pairs. In all, there were 179 low-trauma energy follow-up fractures, of which the most frequent were wrist fractures (33.7%) and malleolar ankle fractures (15.0%). Of the first degree relatives, sisters had 77.7% wrist and 10.6% hip fractures, mothers had 57.5% wrist and 33.7% hip fractures, brother had 36.7% wrist and hip fractures, and fathers had 15.2% wrist and 57.2% hip fractures. The remaining part of the fractures was both hip and wrist fractures or was not specified in the inquiries. The mean age of sibling’s fracture was 53.7 years for sisters and 28.9 years for brothers.
Table 2

Baseline characteristics of the study population (n = 971)

Variable

Mean

SD

Percent

Continuous variables

Age at baseline

53.3

2.9

 

Years since menopause

6.7

4.9

 

BMI

26.3

4.1

 

Nutritional calcium intake

793.2

317.9

 

Parity

2.4

1.5

 

Age at beginning of menstruation

13.8

1.5

 

Baseline T score (FN)

−0.46

1.04

 

Baseline T score (LS)

−0.70

1.27

 

Sibling’s age at fracture

   

 Sister

53.7

11.5

 

 Brother

28.9

16.5

 

Category variables

No HT use during the follow-up

  

49.1

No regular alcohol intake

  

60.2

No smoking

  

91.2

Regular physical activity

  

74.1

Sisters’ fracture

  

9.2

Mothers’ fracture

  

17.7

Brothers’ fracture

  

2.9

Fathers’ fracture

  

3.2

Table 3

Correlation between first degree relatives’ fractures

 

Sister fractures

Mother fractures

Brother fractures

Father fractures

Sister’s fractures

 

0.092**

0.013

0.005

Mother’s fractures

0.092**

 

0.046*

0.009

Brother’s fractures

0.013

0.046*

 

0.077**

Father’s fractures

0.005

0.009

0.077**

 

Pearson bivariate correlations

*p < 0.05; **p < 0.001

Mothers, fathers, or brothers fractures were not significantly associated with fracture-free survival rate in Cox proportional hazards model. Also, having one or more sister or brother was not significantly associated with occurrence of perimenopausal fractures during the 10-year follow-up. Figure 1 shows the adjusted Cox proportional hazards model for fracture-free survival according to sisters’ fractures. In the present study, in the sample of 971 women, 856 women confirmed in the inquiries of having a sister, and further analyses were made on this study sample. Accordingly, in perimenopausal women with sisters with pooled wrist or hip fracture, the fracture-free survival rate was significantly lower in comparison to women with sisters without fracture [HR = 0.56 (0.372–0.844), p = 0.006]. Furthermore, in the sub-analysis on wrist and hip fracture groups, sisters’ hip fracture was a significant predictor of fracture-free survival [HR 0.3 (0.13–0.79), p = 0.013], although there were only ten sisters with a hip fracture. For sisters’ wrist fracture alone, the fracture-free survival rate was non-significantly different between the groups (p = 0.08). In another sub-analysis, the effect of sister’s fractures was similarly oriented for occurrence of perimenopausal wrist fractures [HR 0.52 (0.27–0.99) p = 0.048] in comparison to all follow-up fragility fractures. Of the baseline and adjustment variables presented in Table 2, perimenopausal women with sister’s fractures were slightly older (53.8 vs. 53.3 years, p = 0.041) and had more fractures before the baseline (20.2% vs. 10.3%, p = 0.009) than women without sister’s fractures. There were no differences between the two groups in other adjustment variables.
Fig. 1

Association of sister’s fractures with perimenopausal fracture-free survival during the 15-year follow-up. Cox proportional hazards model adjusted for age, BMI, fracture history (HR 2.05, p < 0.001), duration of follow-up, years since menopause, use of HRT, bone-affecting diseases and medications, smoking, alcohol intake, and nutritional calcium intake. Hazard ratios and p values for significant covariates are shown. HR = 0.56 (0.372–0.844), p = 0.006

Figure 2 depicts the differences in femoral neck T score and its change during the 10-year follow-up according to sister’s wrist or hip fracture. There were no differences between the groups in T score or in its change in co-variance analysis (p = 0.787). Also, Pearson bivariate correlations showed no association between fathers’, mothers’, or brothers’ fractures and low BMD of the perimenopausal women in any of the three follow-up measurements (p < 0.05).
Fig. 2

Association of sister’s fractures with perimenopausal BMD and its change. Analysis of co-variance adjusted for age (p < 0.001), BMI (p = 0.001), fracture history (p < 0.001), duration of follow-up, years since menopause (p < 0.001), use of HRT, bone-affecting diseases and medications, smoking, alcohol intake, and nutritional calcium intake (p = 0.004). p values for significant covariates are shown. p = 0.787 for differences in BMD change between the groups

Table 4 present the Cox proportional hazard models for fracture-free survival in perimenopausal women with or without sister’s wrist or hip fracture according to baseline T score. In women with reported sister’s fracture, baseline femoral neck T score or fracture history before the baseline was not associated with fracture-free survival. In contrast, among women without sister’s wrist or hip fracture, baseline T score was the strongest and significant predictor of fracture-free survival [HR 0.62 (0.5–0.78), p < 0.001]. These effects were unaffected by multiple adjustments (Table 4). Of the adjustment variables, baseline grip strength [p = 0.001, HR 0.28 (0.14–0.58)] was significant only among women with sister’s fracture history. Accordingly, in the full study sample (n = 856), there was a significant interaction in Cox regression model between these two variables [interactive term p = 0.008, HR 0.988 (0.978–0.997), data not depicted].
Table 4

Prediction of fracture risk with baseline BMD in women with and without sisters’ fracture history

 

Beta

Standard error

Significance (p value)

Hazard ratio

95% for HR

Lower

Upper

No sister’s wrist or hip fracture

−0.47

0.12

<0.001

0.62

0.50

0.78

Sister’s wrist or hip fracture

0.51

0.28

0.073

1.66

0.95

2.91

Cox proportional hazards model adjusted for age, body mass index, nutritional calcium intake, fracture history before baseline, alcohol intake, smoking, grip strength, and bone-affecting diseases/medications

Discussion

The present 10-year population-based study investigated the effect of first degree relatives’ fractures on perimenopausal risk of fractures and low BMD in a Finnish cohort of 971 perimenopausal women. Accordingly, reported sister’s life-time wrist or hip fractures (trauma energy not specified) were found to be associated with significantly lower fracture-free survival (including any low-trauma energy fracture type) in contrast to mother’s, brother’s, and father’s wrist or hip fractures. Of the two fracture type, sister’s hip fracture was the stronger predictor of fragility fractures. However, sister’s fracture history was not related to femoral neck T score or its change during the 10-year follow-up. The prediction of perimenopausal fracture-free survival with BMD depended on sister’s fracture history: baseline FN T score predicted fracture-free survival only among women without reported sister’s fractures.

The strengths of the present study included its prospective and population-based nature, large base population, as well as long-term follow-up interval. Bone mass and grip strength measurements were performed under supervision of trained personnel, which may suppress occasional confounders due to measurement errors. Additionally, all the self-reported fractures were validated from the medical records by study group physicians. The source of the present study population of 971 women presented a randomly selected sample, minimizing the possibility of selection bias, while this does not guarantee that the sample was 100% representative of the underlying population. Accordingly, it should be noted that we were forced to exclude some women at the beginning of the study, e.g., since they were labeled to have high-risk profile, described in detail previously [24, 25]. In part, the selection was done for ethical reasons and with regards further exclusions in the case of women with significant BMD measurement errors during the follow-up. The heavy selection procedure may additionally suppress the power to detect small effects of different factors on risk of fractures. The occurrence of bone-affecting diseases and medication, however, were similar between the study group and total population samples, which suggests that the study population would be representative of the base population. Finally, comprehensive adjustment, including a variety of bone-affecting diseases, was used in the multivariate models, which should have weakened the possible bias caused by varying sampling fractions.

A possibility of uncontrolled confounding is always present in epidemiological analyses. The present study was unable to confirm whether the sister with wrist or hip fracture could have been twin sister due to the un-specificity of the questions in the postal inquiries on this matter. Overall, about 2.5% of infants born in Finland are MZ or DZ twins [31] (which of part are male–female pairs), and hence, the possible bias due to this matter may be ignorable. One may also question the validity of the reported relative’s fracture since we were unable to confirm these from, e.g., medical or hospital discharge registers. However, as the questionnaires inquired not only the relatives’ history of wrist or hip fracture itself but also the age of the sister and/or brother at the time of the fracture, the participants were most likely forced to validate the fracture from these specific relatives themselves. Accordingly, the validity of the sister’s or brother’s fracture history may be considered at least moderate in the present study. Considering the parents fracture history, the validity may be poorer and also explain the lack of association with fracture incidence. Another source of inaccuracy was the fact that we could not confirm the circumstance or trauma energy related to the sisters’ or brothers’ fracture accident and neither the exact classification of fracture. Hence, relatives’ hip fracture may have in reality been, e.g., femoral or pelvic fracture and wrist fracture, e.g., in some other region of the forearm, and these may have involved either high- or low-trauma energy. Considering the inquired fractures types (wrist/hip), the typical traumas are fall-related. The mean age of the brothers’ fractures was significantly lower (29 years) in comparison to sisters’ fractures. Accordingly, from this point of view, the brothers’ fractures may have involved greater trauma energy. In contrast, the sisters’ fractures were clearly perimenopausal (mean age 54 years) and thus probably associated with lower trauma energy. Apart from these, another source of inaccuracy may be the definition of menopausal transition, which in the present study, was based purely on self-reports according to amenorrhea without information on hormonal levels. Self-reports, however, have proved to be quite accurate in this matter [32].

Recent studies have found associations between family history of fractures and risk of perimenopausal fractures [10, 11, 12, 13, 14, 15]. Most commonly, it has been reported that family history of hip fractures would be a strong risk factor to a postmenopausal fragility fracture. According to Cummings et al. [15], mothers’ hip fracture was associated with two times higher risk of hip fracture independently of BMD. Another cross-sectional report suggested that first degree relatives’ wrist fracture was associated with an increased risk of low-trauma energy fracture [10]. In the present study, maternal history of fractures was not associated with either perimenopausal rate of fractures or low BMD or bone loss, while sister’s fracture history was a strong risk factor for a fracture. Although our study may not provide explanation for this diversity, previous studies have found that the risk of hip fracture is increased in daughters with maternal history of fragility fractures beyond 50 years of age [33, 34, 35]. In addition, the correlation between family members’ fractures seemed to be sex dependent, i.e., they occurred in father–brother and mother–sister pairs. This may suggest different sources of heritability among male and female subjects. Previously, Kannus et al. have reported in a 25-year follow-up twin study with elderly population that the heritability of fragility fractures would be weaker in Finnish female than male population [36]. The initial reason for the strong association of sister’s fracture history with perimenopausal fragility fracture risk in the present study may be the fact that sisters have the most similar environment, including genotype, with the study population. Among the other first degree relatives, the environmental factors may outweigh the effects of genetic factors. In the 7-year follow-up study of Fox et al. [11], it was found that siblings’ hip fracture increased the risk of hip fractures independently of BMD. Fox et al. also reported that heritance seemed to be fracture type dependent, i.e., family history of wrist or hip fracture increased the risk of wrist or hip fracture, respectively. In addition, other large-scale studies have reported positive correlation of (maternal) family history of fractures, independently of BMD, with fragility fractures [16, 17, 37]

It has been estimated that the heritability of fractures lies between 25% and 35% [18, 19]. From a genetic point of view, it has been claimed that heritability of fragility fracture is independent of BMD [16, 17]. Accordingly, our finding that sister’s or any other family members’ wrist or hip fracture history was not associated with BMD or its change is, in part, in concordance with these previous reports. Several gene candidates have been suggested to be associated with susceptibility of osteoporotic fractures and reviewed recently [38, 39]. The three most extensively studied candidate genes, with regards osteoporotic fracture risk, are vitamin D receptor (VDR), estrogen receptor, and type I collagen. VDR polymorphisms (FokI, BsmI, ApaI, TaqI) has been suggested to be associated with hip and vertebral fractures although not constantly. Similarly, estrogen receptor alpha polymorphism (PvuII, XbaI) may be positively associated with risk of osteoporotic fractures, while negative association have also been reported. In addition, the type I collagen and especially Sp1 polymorphisms may be related in dose-dependent manner with risk of fragility fractures [40]. Finally, several other genes (TGF-beta, IL-6, ApoE) may have associations with BMD and low-trauma energy fractures, while full discussion of these falls beyond the scope of this text.

In all, BMD has been found to explain only 20% of bone fragility among elderly women [3]. Hence, the present study also investigated the ability of bone mineral density to predict fracture risk among women with and without reported sister’s wrist or hip fractures. Among women with sister’s fracture, baseline T score did not predict fragility fractures during the 10-year follow-up. This was in contrast to women without sister’s fractures among which baseline T score was positively associated with occurrence of future fragility fractures. Although the inability of T score to predict fractures in women with sister’s fracture may partly be due to lack of power effect because of small sample size, it may also suggest that other factors, apart from bone density, contribute to genetics-related bone fragility among these women. As an epidemiological study, the present study setting provides insufficient data for direct conclusions on this matter. The observation that grip strength and sister’s fracture history interact in the present study may also be related to ability of muscle strength measures to predict BMD independent qualities of bone.

In conclusion, first degree relatives’ wrist and hip fractures seem to be associated with perimenopausal risk of fragility fractures but not BMD or its change. In the light of the present and previous findings, the exact role of different family members’ fractures on bone fragility remains open. In order to explore this causality accurately, future studies should include data from both genomic samples and long-term population-based follow-up with adequate fracture data. Family members’ fracture history should not be ignored while identifying women at risk for postmenopausal osteoporosis.

Notes

Acknowledgement

The study has been financially supported by the Academy of Finland.

Conflicts of interest

None.

References

  1. 1.
    Dennison E, Cole Z, Cooper C (2005) Diagnosis and epidemiology of osteoporosis. Curr Opin Rheumatol 17(4):456–461PubMedCrossRefGoogle Scholar
  2. 2.
    Cummings SR, Melton LJ (2002) Epidemiology and outcomes of osteoporotic fractures. Lancet 359:1761–1767PubMedCrossRefGoogle Scholar
  3. 3.
    Lindsay R, Meunier PJ (1998) Osteoporosis: review of the evidence for prevention, diagnosis and treatment and cost effectiveness analysis. Status report developed by the National Osteoporosis Foundation. Osteoporosis Int 8(4):S1–S88Google Scholar
  4. 4.
    Kanis JA, Johnell O, De Laet C, Johansson H, Oden A, Delmas P, Eisman J, Fujiwara S, Garnero P, Kroger H, McCloskey EV, Mellstrom D, Melton LJ III, Pols H, Reeve J, Silman A, Tenenhouse A (2004) A meta-analysis of previous fracture and subsequent fracture risk. Bone 35:375–382PubMedCrossRefGoogle Scholar
  5. 5.
    Johnell O, Kanis JA, Oden A, Johansson H, De Laet C, Delmas P, Eisman JA, Fujiwara S, Kroger H, Mellstrom D, Meunier P, Melton LJ III, O’Neill T, Pols H, Reeve J, Silman A, Tenenhouse A (2005) Predictive value of bone mineral density for hip and other fractures. J Bone Miner Res 20:1185–1194PubMedCrossRefGoogle Scholar
  6. 6.
    De Laet C, Kanis JA, Oden A (2005) Body mass index as an indicator of fracture risk. A meta-analysis. Osteoporosis Int 16:1330–1338CrossRefGoogle Scholar
  7. 7.
    Kanis JA, Johansson H, Johnell O, Oden A, De Laet C, Eisman A, Pols H, Tenenhouse A (2005) Alcohol intake as a risk factor for fracture. Osteoporos Int 16:737–742PubMedCrossRefGoogle Scholar
  8. 8.
    Kanis JA, Johnell O, Oden A, Johansson H, De Laet C, Eisman JA, Fujiwara S, Kroger H, McCloskey EV, Mellstrom D, Melton LJ III, Pols H, Reeve J, Silman A, Tenenhouse A (2005) Smoking and fracture risk: a meta-analysis. Osteoporos Int 16:222–228PubMedCrossRefGoogle Scholar
  9. 9.
    Kanis JA, Johansson H, Oden A, Johnell O, De Laet C, Eisman J, McCloskey EV, Mellstrom D, Melton LJ III, Pols H, Reeve J, Silman AJ, Tenenhouse A (2004) A family history of fracture and fracture risk. Bone 35:1029–1037PubMedCrossRefGoogle Scholar
  10. 10.
    Keen RW, Hart DJ, Arden NK, Doyle DV, Spector TD (1999) Family history of appendicular fracture and risk of osteoporosis: a population-based study. Osteoporos Int 10:161–166PubMedCrossRefGoogle Scholar
  11. 11.
    Fox KM, Cummings SR, Powell-Threets K, Stone K (1998) Family history and risk of osteoporotic fracture. Study of Osteoporotic Fractures Research Group. Osteoporos Int 8:557–562PubMedCrossRefGoogle Scholar
  12. 12.
    van der Voort DJ, Geusens PP, Dinant GJ (2001) Risk factors for osteoporosis related to their outcome: fractures. Osteoporos Int 12:630–638PubMedCrossRefGoogle Scholar
  13. 13.
    Siris ES, Miller PD, Barrett-Connor E, Faulkner KG, Wehren LE, Abbott TA, Berger ML, Santora AC, Sherwood LM (2001) Identification and fracture outcomes of undiagnosed low bone mineral density in postmenopausal women: results from the National Osteoporosis Risk Assessment. JAMA 286:2815–2822PubMedCrossRefGoogle Scholar
  14. 14.
    Naves M, Diaz-Lopez JB, Gomez C, Rodriguez-Rebollar A, Cannata-Andia JB (2005) Determinants of incidence of osteoporotic fractures in the female Spanish population older than 50. Osteoporos Int 16:2013–2017PubMedCrossRefGoogle Scholar
  15. 15.
    Cummings SR, Nevitt MC, Browner WS, Stone K, Fox KM, Ensrud KE, Cauley J, Black D, Vogt TM (1995) Risk factors for hip fracture in white women. Study of Osteoporotic Fractures Research Group. N Engl J Med 332:767–773PubMedCrossRefGoogle Scholar
  16. 16.
    Deng HW, Mahaney MC, Williams JT, Li J, Conway T, Davies KM, Li JL, Deng H, Recker RR (2002) Relevance of the genes for bone mass variation to susceptibility to osteoporotic fractures and its implications to gene search for complex human diseases. Genet Epidemiol 22:12–25PubMedCrossRefGoogle Scholar
  17. 17.
    Andrew T, Antioniades L, Scurrah KJ, Macgregor AJ, Spector TD (2005) Risk of wrist fracture in women is heritable and is influenced by genes that are largely independent of those influencing BMD. J Bone Miner Res 20:67–74PubMedCrossRefGoogle Scholar
  18. 18.
    Deng HW, Chen WM, Recker S, Stegman MR, Li JL, Davies KM, Zhou Y, Deng H, Heaney R, Recker RR (2000) Genetic determination of Colles’ fracture and differential bone mass in women with and without Colles’ fracture. J Bone Miner Res 15:1243–1252PubMedCrossRefGoogle Scholar
  19. 19.
    MacGregor A, Snieder H, Spector TD (2000) Genetic factors and osteoporotic fractures in elderly people. Twin data support genetic contribution to risk of fracture. BMJ 320:1669–1670PubMedCrossRefGoogle Scholar
  20. 20.
    Michaelsson K, Melhus H, Ferm H, Ahlbom A, Pedersen NL (2005) Genetic liability to fractures in the elderly. Arch Intern Med 165:1825–1830PubMedCrossRefGoogle Scholar
  21. 21.
    Sirola J, Tuppurainen M, Honkanen R, Jurvelin JS, Kröger H (2005) Associations between grip strength change and axial postmenopausal bone loss—a 10-year population-based follow-up study. Osteoporos Int 16(12):1841–1848, 2005 DecPubMedCrossRefGoogle Scholar
  22. 22.
    Sirola J, Rikkonen T, Tuppurainen M, Jurvelin JS, Kröger H (2006) Association of grip strength change with menopausal bone loss and related fractures: a population-based follow-up study. Calcif Tissue Int 78(4):218–226, AprPubMedCrossRefGoogle Scholar
  23. 23.
    Honkanen R, Tuppurainen M, Alhava E, Saarikoski S (1991) Kuopio Osteoporosis Risk Factor and Prevention Study. Baseline postal inquiry in 1989. Publications of the University of Kuopio, Community Health, Statistics and Reviews 3/1991.Google Scholar
  24. 24.
    Kröger H, Tuppurainen M, Honkanen R, Alhava E, Saarikoski S (1994) Bone mineral density and risk factors for osteoporosis—a population based study of 1600 perimenopausal women. Calcif Tissue Int 55:1–7PubMedCrossRefGoogle Scholar
  25. 25.
    Komulainen MH, Kröger H, Tuppurainen MT, Heikkinen A-M, Alhava E, Honkanen R, Saarikoski S (1998) HRT and vit D in prevention of non-vertebral fractures in postmenopausal women; a 5 year randomised trial. Maturitas 31:45–54PubMedCrossRefGoogle Scholar
  26. 26.
    WHO Scientific Group (1996) Research on the menopause in the 1990’s. A report of the WHO Scientific Group. World Health Organisation, Geneva, Switzerland 866:1–79Google Scholar
  27. 27.
    Honkanen K, Honkanen R, Heikkinen L, Kröger H, Saarikoski S (1999) The validity of self-reports of fractures in perimenopausal women. Am J Epidemiol 150:511–516PubMedGoogle Scholar
  28. 28.
    Sirola J, Kröger H, Honkanen R, Sandini L, Tuppurainen M, Jurvelin JS, Saarikoski S (2003b) Risk factors associated with peri- and postmenopausal bone loss—does HRT prevent weight loss- related bone loss? Osteoporosis Int 14:27–33CrossRefGoogle Scholar
  29. 29.
    Sirola J, Kröger H, Honkanen R, Jurvelin JS, Sandini L, Tuppurainen M, Saarikoski S (2003) Smoking may impair the bone protective effects of nutritional calcium—a population based approach. J Bone Miner Res 18:1036–1043PubMedCrossRefGoogle Scholar
  30. 30.
    Kröger H, Heikkinen J, Laitinen K, Kotaniemi A (1992) Dual-energy X-ray absorptiometry in normal women: a cross-sectional study of 717 Finnish volunteers. Osteoporos Int 2:135–140PubMedCrossRefGoogle Scholar
  31. 31.
    Moilanen I, Ebeling H (1998) To be born as a twin—risks and sequelae. Int J Circumpolar Health 57(2–3):138–147, ReviewPubMedGoogle Scholar
  32. 32.
    Dennerstein L, Smith AMA, Morse C, Burger H, Green A, Hopper J, Ryan M (1993) Menopausal symptoms in Australian women. Med J Aust 159:232–236PubMedGoogle Scholar
  33. 33.
    Bauer DC, Browner WS, Cauley JA, Orwoll ES, Scott JC, Black DM, Tao JL, Cummings SR (1993) Factors associated with appendicular bone mass in older women. The Study of Osteoporotic Fractures Research Group. Ann Intern Med 118:657–665PubMedGoogle Scholar
  34. 34.
    Orwoll ES, Bauer DC, Vogt TM, Fox KM (1996) Axial bone mass in older women. Study of Osteoporotic Fractures Research Group. Ann Intern Med 124:187–196PubMedGoogle Scholar
  35. 35.
    Brot C, Jensen LB, Sorensen OH (1997) Bone mass and risk factors for bone loss in perimenopausal Danish women. J Intern Med 242:505–511PubMedGoogle Scholar
  36. 36.
    Kannus P, Palvanen M, Kaprio J, Parkkari J, Koskenvuo M (1999) Genetic factors and osteoportic fractures in elderly people: prospective 25 year follow-up of nationwide cohort of elderly Finnish twins. BMJ 319:1334–1337PubMedGoogle Scholar
  37. 37.
    Albrand G, Munoz F, Sornay-Rendu E, DuBoeuf, Delmas PD (2003) Independent predictors of all osteoporosis-related fractures in healthy postmenopausal women: The OFELY study. Bone 32:711–717CrossRefGoogle Scholar
  38. 38.
    Lei SF, Jiang H, Deng FY, Deng HW (2007) Searching for genes underlying susceptibility to osteoporotic fracture: current progress and future prospect. Osteoporos Int 18:1157–75PubMedCrossRefGoogle Scholar
  39. 39.
    Ralston S (2002) Genetic control of susceptibility to osteoporosis. J Clin Endocrin Metab 87:2460–2466CrossRefGoogle Scholar
  40. 40.
    Mann V, Ralston SH (2003) Meta-analysis of COL1A1 Sp1 polymorphism in relation to bone mineral density and osteoporotic fracture. Bone 32:711–717PubMedCrossRefGoogle Scholar

Copyright information

© International Osteoporosis Foundation and National Osteoporosis Foundation 2008

Authors and Affiliations

  • J. Sirola
    • 1
    • 5
  • K. Salovaara
    • 1
    • 5
  • M. Tuppurainen
    • 1
    • 2
  • J. S. Jurvelin
    • 3
  • E. Alhava
    • 4
  • H. Kröger
    • 1
    • 5
  1. 1.Bone and Cartilage Research Unit (BCRU)University of KuopioKuopioFinland
  2. 2.Department of Obstetrics and GynaecologyKuopio University HospitalKuopioFinland
  3. 3.Department of Clinical Physiology & Nuclear MedicineKuopio University HospitalKuopioFinland
  4. 4.Department of SurgeryKuopio University HospitalKuopioFinland
  5. 5.Department of Orthopedics and TraumatologyKuopio University HospitalKuopioFinland

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