Elsevier

Bone

Volume 50, Issue 6, June 2012, Pages 1288-1293
Bone

Original Full Length Article
Effect of co-morbidities on fracture risk: Findings from the Global Longitudinal Study of Osteoporosis in Women (GLOW)

https://doi.org/10.1016/j.bone.2012.02.639Get rights and content

Abstract

Introduction

Greater awareness of the relationship between co-morbidities and fracture risk may improve fracture-prediction algorithms such as FRAX.

Materials and methods

We used a large, multinational cohort study (GLOW) to investigate the effect of co-morbidities on fracture risk. Women completed a baseline questionnaire detailing past medical history, including co-morbidity history and fracture. They were re-contacted annually to determine incident clinical fractures. A co-morbidity index, defined as number of baseline co-morbidities, was derived. The effect of adding the co-morbidity index to FRAX risk factors on fracture prevention was examined using chi-squared tests, the May–Hosmer test, c index and comparison of predicted versus observed fracture rates.

Results

Of 52,960 women with follow-up data, enrolled between October 2006 and February 2008, 3224 (6.1%) sustained an incident fracture over 2 years. All recorded co-morbidities were significantly associated with fracture, except for high cholesterol, hypertension, celiac disease, and cancer. The strongest association was seen with Parkinson's disease (age-adjusted hazard ratio [HR]: 2.2; 95% CI: 1.6–3.1; P < 0.001). Co-morbidities that contributed most to fracture prediction in a Cox regression model with FRAX risk factors as additional predictors were: Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, osteoarthritis, and heart disease.

Conclusion

Co-morbidities, as captured in a co-morbidity index, contributed significantly to fracture risk in this study population. Parkinson's disease carried a particularly high risk of fracture; and increasing co-morbidity index was associated with increasing fracture risk. Addition of co-morbidity index to FRAX risk factors improved fracture prediction.

Highlights

► We investigated the effect of co-morbidities on fracture risk. ► 3224 of 52,960 women (6.1%) sustained an incident fracture over 2 years. ► Parkinson's disease and multiple sclerosis had the greatest effect on fracture risk.

Introduction

Since its launch, the fracture-prediction algorithm, FRAX, has been in constant evolution to improve its predictive capacity internationally [1]. It has been suggested that further collection of information regarding co-morbidities may be helpful in this process. At present, the investigator is asked to provide information on the presence of rheumatoid arthritis, and to consider whether a number of conditions associated with “secondary osteoporosis” are present. Examples given are inflammatory bowel disease, insulin-dependent diabetes, and diseases associated with reduced mobility, such as stroke and Parkinson's disease. However, a number of other co-morbidities have been shown to be associated with fracture. For example, some papers have reported an excess risk of cardiovascular disease among patients with low bone density [2], [3]. The cause of this association is likely to be multifactorial, representing a combination of the disease process itself (ongoing inflammatory process and sex hormone deficiency) and lifestyle factors (poor mobility and tobacco use). Other studies suggest that there is an increased risk of fracture among patients with respiratory disease that cannot be explained by steroid use alone [4], [5]; while other co-morbidities, such as Parkinson's disease, may be associated with a significantly increased risk of falling.

We used a large, multinational cohort study to investigate the size of the effect of single co-morbidities on fracture risk, and specifically to investigate whether the number of co-morbidities present might also be an important determinant of fracture risk. Finally, we also considered whether incorporation of further information on medical history by means of generation of a ‘co-morbidity index’ might improve fracture prediction by the FRAX algorithm.

Section snippets

Setting

GLOW is an observational cohort study that is being conducted in physician practices at 17 sites in 10 countries (Australia, Belgium, Canada, France, Germany, Italy, Netherlands, Spain, U.K., and U.S.). These sites are located in major population centers. Clinical investigators at each of the 17 sites constitute the GLOW Scientific Advisory Board and are responsible for the management of the study. Details of the study design and methods have been previously described [6]. In brief, practices

Results

A total of 60,393 patients from the practices of 723 physicians were enrolled in the study between October 2006 and February 2008. Approximately 25,000 women were recruited from 274 physician practices in Europe; 28,000 from 255 practices in the U.S.; and almost 7000 from 86 practices in Canada and Australia. Of the 52,960 women with follow-up data, 3224 (6.1%) sustained an incident fracture over 2 years of follow-up. Co-morbidities were common: 26,215 women (49.5%) reported hypertension and

Discussion

In this study, all recorded co-morbidities were significantly associated with fracture, except for high cholesterol, hypertension, celiac disease, and cancer. The strongest association was seen with Parkinson's disease. Further analyses suggested that addition of information on co-morbidities to the conventional FRAX risk factors was a valuable addition, although one comparison (the c index) suggested that this was not the case. Other studies, however, have suggested that this statistic may not

Role of the funding source

Financial support for the GLOW study is provided by Warner Chilcott Company, LLC and sanofi-aventis to the Center for Outcomes Research, University of Massachusetts Medical School. The sponsors had no involvement in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to submit the article for publication. The design, conduct, and interpretation of the GLOW data are undertaken by an independent steering committee.

Acknowledgments

We thank the physicians and project coordinators participating in GLOW.

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