A Meta-Analysis of the Association of Fracture Risk and Body Mass Index in Women

Johansson, Helena; Kanis, John А.; Odén, Anders; McCloskey, Eugène; Chapurlat, Roland; Christiansen, Claus; Cummings, Steve; Díez-Pérez, Adolfo; Eisman, John A.; Fujiwara, Saeko; Glüer, Claus C.; Goltzman, David; Hans, Didier; Khaw, Kay Tee; Krieg, Marc Antoine; Kröger, Heikki; LaCroix, Andrea Z.; Lau, Eyy; Leslie, William D.; Mellström, Dan; Melton, L. Joseph; O'Neill, Terence W; Pasco, Julie A.; Prior, Jerilynn C.; Reid, David M.; Rivadeneira, Fernando; Staa, Tjeerd van; Yoshimura, Nagahisa; Zillikens, M. Carola

doi:10.1002/jbmr.2017

Cited by 419 publications

(338 citation statements)

References 63 publications

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“…It is also of interest that obesity (BMI ≥30 kg/m 2 ) may reduce the risk of hip fractures in women 29. Additionally, the prognosis of COPD is worsened by the presence of comorbid illnesses such as hypertension and diabetes mellitus 30.…”

Section: Discussionmentioning

confidence: 99%

Association Between Life's Simple 7 and Noncardiovascular Disease: The Multi‐Ethnic Study of Atherosclerosis

Ogunmoroti

Allen

Cushman

et al. 2016

JAHA

107

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BackgroundThe American Heart Association introduced the Life's Simple 7 (LS7) metrics to assess and promote cardiovascular health. We examined the association between the LS7 metrics and noncardiovascular disease.Methods and ResultsWe studied 6506 men and women aged between 45 and 84 years, enrolled in the Multi‐Ethnic Study of Atherosclerosis. Median follow‐up time was 10.2 years. Each component of the LS7 metrics (smoking, body mass index, physical activity, diet, total cholesterol, blood pressure, and blood glucose) was assigned points, 0 indicates “poor” category; 1, “intermediate,” and 2, “ideal.” The LS7 score, ranged from 0 to 14, was created from the points and categorized as optimal (11–14), average (9–10), and inadequate (0–8). Hazard ratios and event rates per 1000 person‐years were calculated for outcomes based on self‐reported hospitalizations with the International Classification of Diseases, 9th Revision, diagnoses of cancer, chronic kidney disease, pneumonia, deep venous thromboembolism/pulmonary embolism, chronic obstructive pulmonary disease, dementia, and hip fracture. Analyses were adjusted for age, sex, race/ethnicity, income, and education. Overall, noncardiovascular disease event rates were lower with increasing LS7 scores. With the inadequate LS7 score as reference, an optimal score was associated with a decreased risk for noncardiovascular disease events. The hazard ratio for cancer was, 0.80 (0.64–0.98); chronic kidney disease, 0.38 (0.27–0.54); pneumonia, 0.57 (0.40–0.80); deep venous thromboembolism/pulmonary embolism, 0.52 (0.33–0.82), and chronic obstructive pulmonary disease, 0.51 (0.31–0.83).ConclusionsThe American Heart Association's LS7 score identified individuals who were vulnerable to multiple chronic nonvascular conditions. These results suggest that improving cardiovascular health will also reduce the burden of cancer and other chronic diseases.

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Section: Discussionmentioning

confidence: 99%

Association Between Life's Simple 7 and Noncardiovascular Disease: The Multi‐Ethnic Study of Atherosclerosis

Ogunmoroti

Allen

Cushman

et al. 2016

JAHA

107

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“…15,79,80 A BMI of o21 kg m À 2 or a body weight of o127 pounds is associated with increased risk of low BMD and increased fracture risk in women. 15 There is a strong correlation between BMD and BMI, and several studies have shown that a decrease in body weight leads to bone loss.…”

Section: Body Mass Indexmentioning

confidence: 99%

“…81 A recent meta-analysis investigated the association between BMI and future fracture risk at different skeletal sites and concluded that low BMI remains a risk factor for hip and all osteoporotic fractures. 80 Even when adjusted for BMD, low BMI remains a risk factor for hip fracture. 15,80 BMI is more predictive of osteoporosis than is weight alone.…”

Section: Body Mass Indexmentioning

confidence: 99%

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A review of lifestyle, smoking and other modifiable risk factors for osteoporotic fractures

Abrahamsen¹,

Brask-Lindemann

Rubin

et al. 2014

BoneKEy Reports

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Although many strong risk factors for osteoporosis-such as family history, fracture history and age-are not modifiable, a number of important risk factors are potential targets for intervention. Thus, simple, non-pharmacological intervention in patients at increased risk of osteoporotic fractures could include reduction of excessive alcohol intake, smoking cessation, adequate nutrition, patient education, daily physical activity and a careful review of medications that could increase the risk of falls and fractures. There remains, however, an unmet need for high-quality intervention studies in most of these areas.

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“…(14)(15)(16) A recent meta-analysis examined the association between BMI and fracture risk in prospective cohorts from 25 countries (398,610 women with an average age of 63 years; 2.2 million person-years follow-up) with a reported 22% prevalence of obesity. (17) Risk for major osteoporotic fractures and hip fractures decreased monotonically with increasing BMI when the modifying effect of BMD was excluded. When BMD was considered, the effect of BMI was greatly reduced and, at least for major osteoporotic fractures, showed a biphasic response such that fracture risk decreased as BMI increased to the normal range and then slightly increased for overweight and obese individuals.…”

mentioning

confidence: 94%

Untangling the Causality Knot: Another Tool for Clinical Researchers

Lix

Leslie

2014

Journal of Bone and Mineral Research

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Happy the man who has been able to learn the causes of things. -Virgil Clinical experience and a sound knowledge of pathophysiology are the traditional pillars of medicine. In recent years, new statistical methods have strengthened the ability of investigators to extract patterns from complex datasets that would otherwise remain hidden. Research to predict clinical events and the methods needed to support these analyses have evolved in tandem over the last few decades in a classical pas de deux. Indeed, the landscape of modern medicine would hardly be recognizable without such techniques. The recent development of fracture prediction tools is a prime example of one clinical application of powerful statistical methods.(1)When prediction of an event is the sole objective, the existence of an association-whether causal or not-may be sufficient. However, to understand how that association arises, and to intervene appropriately, requires a deeper understanding of the cause and effect relationships. Obesity, defined from elevated body mass index (BMI), has generated much controversy in the osteoporosis literature, variably reported to be protective, neutral, or a risk factor for osteoporosis and related fractures.(2-4) Weight and BMI are both strongly correlated with bone mineral density (BMD) as measured from dual-energy X-ray absorptiometry (DXA) in almost all studies, and it is no accident that weight or BMI are a component of virtually all fracture prediction tools.(1) However, when weight is partitioned into its components of lean mass and fat mass, the associations become more complicated. Most studies find that lean mass is positively associated with higher BMD. (5,6) For fat mass a variety of results have been found, (5,(7)(8)(9)(10)(11)(12) although a recent metaanalysis demonstrates a positive association between fat mass and bone density.(13) Meta-analytic results have also shown that among premenopausal women lean mass exerted a greater effect on femoral neck BMD than fat mass (r ¼ 0.45 versus r ¼ 0.30), whereas in postmenopausal women the effects of lean mass and fat mass were similar (r ¼ 0.33 versus r ¼ 0.31).(13) The presence of large correlations among weight, BMI, lean mass, and fat mass have fueled some of this controversy.(4) For example, if one assumes that lean mass is solely responsible for BMD and that fat mass has no effect, then a model that includes weight and fat mass would actually show the latter as having an adverse (negative) effect on BMD simply because fat is interpreted as "not lean" in the model. Body composition is a major factor impacting accuracy errors in DXA, effects that are amplified in longitudinal studies where weight, body composition, and BMD (not to mention a myriad of other factors) are changing simultaneously. (14)(15)(16) A recent meta-analysis examined the association between BMI and fracture risk in prospective cohorts from 25 countries (398,610 women with an average age of 63 years; 2.2 million person-years follow-up) with a reported 22% prevalence of obesity.(17) ...

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A Meta-Analysis of the Association of Fracture Risk and Body Mass Index in Women

Cited by 419 publications

References 63 publications

Association Between Life's Simple 7 and Noncardiovascular Disease: The Multi‐Ethnic Study of Atherosclerosis

Association Between Life's Simple 7 and Noncardiovascular Disease: The Multi‐Ethnic Study of Atherosclerosis

A review of lifestyle, smoking and other modifiable risk factors for osteoporotic fractures

Untangling the Causality Knot: Another Tool for Clinical Researchers

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