Abstract
Context
Parental history of osteoporosis is associated with an increased risk of fracture. However, there are not many data on the mechanism of action.
Our objective was to determine if heredity influences fracture rate: independently or through the bone mineral density; to identify also the strongest independent risk factors of osteoporotic fractures among our study population.
Methods
We processed data of 541 women outpatients with an average age of 55 years, participating in an osteoporosis screening program.
Our results confirm that the presence of family history significantly increases fracture prevalence, (37% vs. 17%, p<0.001, OR 2.853, p=0.001) and decreases BMD scores. Fractures occur at higher (better) T and Z-scores. The risk of having T values in the range of (0- -1) and Z values in (-1--2) is much higher in the positive group. The logistic regression analysis confirms the BMD-independent influence of heredity on fracture risk.
Conclusions
Parental history of osteoporosis negatively affects bone density and significantly increases the incidence of fractures. The latter happens also independently of the bone density values. Timely intervention in these easy-to-detect cases may be the most effective prevention of osteoporotic fractures.
Keywords: family history, fractures, mineral density, osteoporosis
Introduction
Osteoporosis is a systemic skeletal disease, characterized by low bone density, deterioration of bone microarchitecture and increased risk of fracture (1).
Bone remodeling is a process of bone reconstruction that persists throughout life, consisting of repairing microlesions and maintaining bone strength. Annually, 5-10% of an adult’s skeleton is renewed through this process. It has been estimated that 3–4 million bone remodeling units are initiated each year (2). The most important factors affecting the risk of fracture are: age, sex, race, body mass index below 20, the presence of fragility fracture in the patient’s history or in family history, smoking, long-term corticosteroid therapy, alcohol consumption, rheumatoid arthritis, secondary osteoporosis and coffee consumption (3).
A family history of fragility fracture consolidates the genetic cause of osteoporosis. Studies on families and twins show that genetic factors play an important role in the development of bone density. More factors that affect the risk of fracture are inherited, such as the geometry of the femoral neck, the length of the hip axis, biophysical markers of bone remodeling, body mass index, and the age of onset of first menstruation and menopause (4). The risk of fracture of the tibia remained high even after correcting data with bone density values in those whose mother or sister suffered fractures. The same can be seen in the case of a fracture of the wrist. Thus, there might be a genetic component of fracture risk, which is independent of bone density (5). The practical importance of the results lies in the fact that the treatment of women with a family history of fracture can be initiated at a less strict bone density threshold, and can increase patient cooperation in the case of subsequent interventions (6).
Bone density is characterized by T and Z scores measured by DXA. The T-score is the compared value of the bone mineral density of an individual with a healthy 30-year-old adult, the Z score is the deviation from the bone density value corresponding to the sex and age of the individual, both expressed in standard deviation (7). The World Health Organization determines the severity of osteoporosis based on the T-value: normal bone density (T> = - 1SD osteopenia (-2.5SD <T <-1SD), osteoporosis (T <= - 2.5SD), advanced osteoporosis (T <= - 2.5SD + one or more osteoporotic fractures in medical history) (8).
One of the most serious consequences of osteoporosis is the fragility fracture. Nowadays, more attention is paid to the assessment of risk factors influencing fracture risk than to diagnose low bone mass only. People with genetic predisposition to osteoporosis should be timely identified and urged to be screened with the FRAX- fracture risk calculation model and a bone mineral density measurement (7).
The purpose of this study was to check the magnitude of the genetic determination, comparing the impact of a positive family history on fracture risk with other well-known risk factors, as coffee consumption, cigarette smoking, prolonged glucocorticoid therapy, sedentary lifestyle, early menopause and body mass index less than 20 kg/m2.
We examined four correlations:
How does the family history of fracture affect the fracture risk of an individual?
In the case of a positive family history: how the bone mineral density values are influenced?
At what values of the bone density do the fractures occur in the two groups: with or without a positive family history.
Is heredity independently affecting fractures or via bone density?
METHODS
In our retrospective study, we processed data from 541 women who volunteered for osteoporosis screening between 2005 and 2019. Characteristics of the surveyed individuals are summarized in Table 1. They all completed a questionnaire assessing the presence of the following risk factors: coffee consumption, smoking, long-term glucocorticoid therapy, sedentary lifestyle, early menopause, body mass index, presence or absence of a family history (one of the parents) of fracture. We examined the correlations between fractures, T and Z values and the family history of fragility fractures. The bone density was measured with an ultrasound device (ALOKA-AOS 100NV).
Table 1.
| BMD range | <= -2 | -2 to-1.5 | -1.49 to-1 | -0.99 to-0.5 | -0.49 to 0 | 0 <= |
|---|---|---|---|---|---|---|
| No. of cases | 55 | 106 | 115 | 140 | 81 | 36 |
| Significance | 0.151 | 0.163 | 0.002 | 0.154 | 0.432 | |
| % Difference | 20.5 | 12.4 | 30.2 | 12.1 | 4 |
Data was processed with IBM SPSS 17.0 statistics, using the following tests: T test, Chi-square test, risk estimation, regression analysis. All tests were performed with a 95% confidence interval; a p value less than 0.05 was considered statistically significant.
Exclusion criteria for subjects selected for the study: men, individuals with fractures due to other diseases (poliomyelitis, osteogenesis imperfecta, bone metastasis), fractures due to major trauma, lack of personal consent, individuals with an incomplete questionnaire.
The following hypotheses (H1) were established:
H1A: a higher incidence of bone fractures with a positive family history
H1B: Z and T values are lower in those with a positive family history
H1C: family history affects fracture risk independently of bone density.
RESULTS
Characteristics of the study population and global results of the fracture risk assessment are included in Table 1.
We analyzed the distribution of T and Z values, grouping patients in different T/Z value ranges: below -2, between -2 and -1, between -1 and 0 and higher values than 0. The number of patients in each range according to the family history (+ or -) can be seen in Figure 1 and Figure 2.
Figure 1.
The percentage distribution of T values in the group of patients with positive and negative family history.
a. for those with a positive family history, we found a prevalence of T values in the range -1 to -2 16% higher.
b. higher prevalence of women with a negative family history was found in the moderated (healthy) T value ranges (13% higher in (-1 to 0) and 5% higher in T>0).
c. the average T value in the case of a negative family history is -0.99 compared to those with a positive family history: -1.19.
Figure 2.
The percentage distribution of Z values in the group of patients with positive and negative family history.
a. observing the Z values, in the range -1 to -2 women with family history are present with a surplus of 8% and a minus 8% in the healthy range of >0.
b. the average Z score in the case of a negative family history is -0.45 compared to -0.64 of those with positive family history (p = 0.017).
We further focused on the patients with a fragility fracture only, dividing the BMD ranges with high prevalence of fractures (between -2 and 0) in four intervals to assess if/where there is a significant difference between the two groups: with a positive vs. negative family history. Range T score >0 and Z scores <2 are not illustrated due to the low rate of fractures.
When evaluating results based on the T values, we found a highly significant difference (p=0.002) in the range -1.49 and -1, with a total of 115 cases and a difference of 30.2% between the two. Other ranges differed however not significantly (Fig. 3).
Figure 3.
The rate of fractures based on T values in the case of the two groups of women with positive and negative family history.
a. among those with a positive family history, we found higher T values in all ranges.
b. a highly significant difference (p=0.002) was observed in the range -1.49 to -1, with a total of 115 cases and a percentage difference of 30.2.
Based on Z values, the highest represented ranges are the one -0.99 – 0.5 and -0.5 – 0, both with significant difference between patients with or without a family history (p=0.002 and 0.003), differences of 30.4% and 26.8% between the fractured cases (Fig. 4).
Figure 4.
The rate of fractures based on Z values in the case of the two groups of women with or without a family history.
a. the highest represented ranges of the Z values are: -0.99 to – 0.5 and -0.5 to 0, both with significant difference between the patients with and without a family history (p=0.002 and 0.003), differences of 30.4% and 26.8% between the fractured cases.
In order to keep multicollinearity under control, we used logistic regression analysis to assess the impact of the risk factors on the fracture risk. Results are included in Table 2. Z score, family history and age were found to be statistically significant on the fracture rate. The highest OR was found for the variables: positive family history (2.866), lowest, 0.512 for the Z score. Smoking, just close to significance (0.066) with a ratio of 1.764.
Table 2.
| BMD range | < 2 | -2 to-1.5 | -1.49 to-1 | -0.99 to-0.5 | -0.49 to 0 | 0 < |
|---|---|---|---|---|---|---|
| Nr of cases | 7 | 45 | 101 | 126 | 129 | 119 |
| Significance | 0.151 | 0.373 | 0.002 | 0.003 | 0.294 | |
| % Difference | 20.3 | 5.6 | 30.4 | 26.8 | 6.8 |
DISCUSSION
We observed the fracture rate increasing over the age of 50, with the highest percentage among women aged 60 – 69 (40%). This is followed by the group of women between 70 and 79 years, with a prevalence of fracture of 33%.
A family history of osteoporotic fractures was reported by 118 (22%) women. Using the Mann Whitney test, we observed a statistically significant increase of fractures in those with a family history of fracture, an increase of 1% in women aged 50-69 years (p=0.003), an increase of 5% in those with age between 70 – 79 years (p=0.005); 37% of the subjects with a positive family history have had a fracture, compared to those with a negative family history: with 17% (71 women).
We examined the percentage distribution of T and Z values in the two groups. For those with a positive family history, we found a 16% higher prevalence of T values in the range -1 to -2. Higher prevalence of women with a negative family history was found in the moderated (healthy) T value ranges (13% higher in (-1 to 0) and 5% higher in T>0). The average T value in the case of a negative family history is -0.99 compared to those with a positive family history: -1.19 (p = 0.009) (Fig. 1).
Observing the Z values, in the range -1 to -2 women with family history are present with a surplus of 8% and a minus of 8% in the healthy range of >0. The average Z score in the case of a negative family history is -0.45 compared to -0.64 of those with positive family history (p = 0.017) (Fig. 2).
We finally checked the distribution of fractures based on T and Z values in the case of the two groups. Among those with a positive family history, we found higher T values in all ranges, but a highly significant difference (p=0.002) was observed in the range -1.49 to -1, with a total of 115 cases and a percentage difference of 30.2 (Fig. 3).
The highest represented ranges of the Z values are: -0.99 to – 0.5 and -0.5 to 0, both with a significant difference between the patients with and without a family history (p=0.002 and 0.003), differences of 30.4% and 26.8% between the fractured cases (see Fig. 4).
To verify our first hypothesis, we observed how fractures are distributed among the group of women with a positive and negative family history. The incidence of fractures is 20% higher in the case of a positive family history. They are more than twice as likely to develop fractures as those with a negative family history (OR = 2.853, p <0.001) (Table 2).
To verify the second hypothesis, we analyzed whether there is a significant difference between the T and Z values of women with a positive family history compared to those without a history of family fracture. We found that the average values of T and Z scores, for those with a family history, were 17% and 30% lower (Figs 1 and 2). Our results are statistically significant both in the case of T (p = 0.0093) and Z values (p = 0.0173). M. J. Grainge and colleagues (9) investigated the relationship between family history and bone density in menopausal women; 52.8% of the 580 women studied had a family history of fracture. Based on their results, the mean T value in women with a positive family history was 8.9% lower compared to the comparison group (p <0.05).
We compared the values of T and Z scores, where the fractures occur in the two groups. Most fractures in those without a family history occur at lower BMD scores (T below -2 and at Z below -1) (Figs 3 and 4), than among those having a positive family history (T -1 and -2 and Z values between 0 and -1). The correlation between fractures and bone density values in the two groups was not statistically significant (p = 0.94; p = 0.97).
We used logistic regression analysis to double-check our third hypothesis: the independent effect of each main risk factor on fracture. Results show that family history independently influences fracture risk with an OR of 2.853, meaning that having a family history of fractures will increase by almost three fold the risk of an individual to suffer a fracture. This effect is independent of the family history’s effect on the bone mineral density. Age adjusted bone mineral density, represented by the Z score, will also influence fracture risk with an OR of 0.512, meaning that an increase in Z score of 1 SD will decrease fracture risk to almost half. This analysis also shows that age influences independently the fracture risk, not only by affecting the bone mineral density, confirming the impact of other age related factors, like sarcopenia or risk of fall.
Several other studies suggest that family history of fractures increase the risk of fragility fracture representing a strong predictor for the first – incident fracture, especially of the hip (10-13). Bijelic et al., found a similar OR=2,875 (p=0.003) in a univariate logistic regression analysis, while their results of multivariate logistic regression show a much higher ratio (OR=4.567; p=0.003) for family history as an independent risk factor for the occurrence of osteoporosis in postmenopausal women (12).
Robitaille et al. differentiated inheritance separately grouping maternal history and 1, 2 or more family members with osteoporosis. Compared with women without a family history of osteoporosis, adjusted OR was 1.84 for those reporting one affected family member and 8.48 (95% CI=4.50, 15.99) for women with two or more affected family members. Furthermore, the risk for women who reported having a mother with osteoporosis was higher than that for women without family history of osteoporosis (OR 2.81) (14). The effect of different risk factors on fracture was also assessed in a Brazilian cross-sectional study involving 4332 women over 40 years. Age (OR = 1.05), family history (OR = 3.59) and low body mass index (OR = 2.28) statistically increased the risk of fracture (15).
Other studies meantime found no significant difference between postmenopausal women with and without osteoporosis with regard to family history of osteoporosis (16,17).
Following independent validation, FRAX has been incorporated in more than 80 guidelines worldwide (18) being a more predictive tool than DXA alone. In addition, diabetes is a considerable risk factor for osteoporosis (19). Taking into consideration the impact of heredity on fracture risk, we insist on using the FRAX model as an efficient screening tool to appreciate fracture risk also by BMD independent risk factors.
Validity of our results may be affected by the fact that we used an ultrasound machine to assess bone mineral density, instead of a DXA scan, considered the gold standard method. We did not have specific information regarding the site of the fractures or the path of the inheritance (if there was a maternal or a paternal history of fracture).
With this study, we would like to draw attention to the importance of a positive family history, as the most important risk factor, to signal both: patients and clinicians to control further evolution of the process and thus reduce the incidence of fractures in these patients.
In conclusion, these data suggest that a positive family history negatively affects bone density and significantly increases the incidence of fractures. The BMD ranges with high prevalence of fractures among those with a family history and the highest difference between the two groups is T (-1.49 to -1) and Z (-0.99 to -0.5). A positive family history affects fracture risk regardless of bone density values. The importance of a positive family history relies on the fact that everyone should be aware of the vulnerable group, to be able to prevent osteoporotic fractures by timely intervening in this process (changing lifestyle, taking medication). Case finding strategies should adopt screening methods which also include BMD independent parameters.
Conflict of interest
The authors declare that they have no conflict of interest.
References
- 1.Kanis JA, McCloskey EV, Johansson H, Cooper C, Rizzoli R, Reginster JY. European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos Int. 2013;24:23–57. doi: 10.1007/s00198-012-2074-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Langdahl B, Ferrari S, Dempster DW. Bone modeling and remodeling: potential as therapeutic targets for the treatment of osteoporosis. Ther Adv Musculoskelet Dis. 2016;8:225–235. doi: 10.1177/1759720X16670154. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Unnanuntana A, Gladnick BP, Donnelly E, Lane JM. The Assessment of Fracture Risk. J Bone Joint Surg Am. 2010;92:743–753. doi: 10.2106/JBJS.I.00919. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Stewart TL, Ralston SH. Role of genetic factors in the pathogenesis of osteoporosis. J Endocrinol. 2000;166:235–245. doi: 10.1677/joe.0.1660235. [DOI] [PubMed] [Google Scholar]
- 5.Tuan D, Nguyen V, Eisman JA. Genetics of Fracture: Challenges and Opportunities. J Bone Miner Res. 2000;15:1253–1256. doi: 10.1359/jbmr.2000.15.7.1253. [DOI] [PubMed] [Google Scholar]
- 6.Melton LJ, Thamer M, Ray NF, Chan JK, Chesnut CH, Einhorn TH, Johnston CC, Raisz LG, Silverman SL, Siris ES. Fractures attributable to osteoporosis: report from the National Osteoporosis Foundation. J Bone Miner Res. 1997;12:16–23. doi: 10.1359/jbmr.1997.12.1.16. [DOI] [PubMed] [Google Scholar]
- 7.Kanis JA, McCloskey EV, Johansson H, Cooper C, Rizzoli R, Reginster JR. European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos Int. 2013;24:23–57. doi: 10.1007/s00198-012-2074-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Kanis JA, Melton J, Christiansen C, Johnston CC, Khaltaev N. The diagnosis of osteoporosis. J Bone Miner Res. 1994;9:1137–1141. doi: 10.1002/jbmr.5650090802. [DOI] [PubMed] [Google Scholar]
- 9.Grainge MJ, Coupland CAC, Cliffe SJ, Chilvers CE, Hosking DJ. Association Between a Family History of Fractures and Bone Mineral Density in Early Postmenopausal Women. Bone. 1999;24:507–512. doi: 10.1016/s8756-3282(99)00016-2. [DOI] [PubMed] [Google Scholar]
- 10.Kanis JA, Johansson H, Oden A, Johnell O, De Laet C, Eisman JA, McCloskey EV, Mellstrom D, Melton LJ, Pols HAP, Reeve J, Silman AJ, Tenenhouse A. A family history of fracture and fracture risk: a meta-analysis. Bone. 2004;35:1029–1037. doi: 10.1016/j.bone.2004.06.017. [DOI] [PubMed] [Google Scholar]
- 11.Fox KM, Cummings SR, Powell-Threets K, Stone K. Family History and Risk of Osteoporotic Fracture. Osteoporos Int. 1998;8:557–562. doi: 10.1007/s001980050099. [DOI] [PubMed] [Google Scholar]
- 12.Bijelic R, Milicevic S, Balaban J. The Influence of Non-preventable Risk Factors on the Development of Osteoporosis in Postmenopausal Women. Mater Sociomed. 2019;31:62–65. doi: 10.5455/msm.2019.31.62-65. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Lorentzon M, Branco J, Brandi ML, Bruyère O, Chapurlat R, Cooper C, Cortet B, Diez-Perez A, Ferrari S, Gasparik A, Herrmann M, Jorgensen NR, Kanis J, Kaufman JM, Laslop A, Locquet M, Matijevic R, McCloskey E, Minisola S, Pikner R, Reginster JY, Rizzoli R, Szulc P, Vlaskovska M, Cavalier E. Algorithm for the Use of Biochemical Markers of Bone Turnover in the Diagnosis, Assessment and Follow-Up of Treatment for Osteoporosis. Adv Ther. 2019;36:2811–2824. doi: 10.1007/s12325-019-01063-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Robitaille J, Yoon PJ, Moore CA, Liu T, Irizarry-Delacruz M, Looker AC, Khoury MJ. Prevalence, Family History, and Prevention of Reported Osteoporosis in U. S. Women. Am J Prev Med. 2008;35:47–54. doi: 10.1016/j.amepre.2008.03.027. [DOI] [PubMed] [Google Scholar]
- 15.Shu MM, Canhos AL, Ocampos GP, Plapler PG, Camargo OP, Rezende MU. Profile of Patients with osteoporotic fractures at a Tertiary Ortopedic Trauma Center. Acta Ortop Bras. 2018;26:117–122. doi: 10.1590/1413-785220182602185325. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Dandan X, Yinghui Z, Yan Z, Shanjiang F, Sang F, Xiyu W, Yulin M, Zhifeng S. Osteoporosis screening based on body mass index, years since menopause and age among postmenopausal women in South Central China. Int J Clin Exp Med. 2018;11:2543–2550. [Google Scholar]
- 17.Chen FP, Fu TS, Lin YC, Fan CM. Risk factors and quality of life the occurrence of hip fracture in postmenopausal women. Biomed J. 2018;41:202–208. doi: 10.1016/j.bj.2018.04.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Kanis JA, Harvey NC, Johansson H, Liu E, Vandenput L, Lorentzon M, Leslie WD, McCloskey E. V. A decade of FRAX: how has it changed the management of osteoporosis? Aging Clin Exp Res. 2020;32:187–196. doi: 10.1007/s40520-019-01432-y. [DOI] [PubMed] [Google Scholar]
- 19.Poiana C, Capatina C. Osteoporosis and Fracture Risk in Patients with Type 2 Diabetes Mellitus. Acta Endocrinologica-Bucharest. 2019;15(2):231–236. doi: 10.4183/aeb.2019.231. [DOI] [PMC free article] [PubMed] [Google Scholar]