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Record W2117281253 · doi:10.1093/eurheartj/ehp345

A new pathway? Failure, fragility and fractures

2009· letter· en· W2117281253 on OpenAlexafffund
Justin A. Ezekowitz

Bibliographic record

VenueEuropean Heart Journal · 2009
Typeletter
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMedicineHeart failureOsteoporosisOsteopeniaMyocardial infarctionEpidemiologyPopulationIncidence (geometry)PediatricsSurgeryInternal medicineBone mineral

Abstract

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Heart failure is a leading cause of hospitalization and mortality in Europe and North America.1–3 Parallel with public health initiatives that have successfully enhanced treatment rates of hypertension and survival after myocardial infarction, a delay in the incidence of heart failure has supervened. Hence the median age of heart failure patients in clinical trials and large epidemiological studies ranges between ∼65 and 75 years of age, and such patients are at risk for other co-morbid conditions either causally related or as epiphenomena. Osteoporosis is one such co-morbidity, affecting 1 in 4 women and 1 in 8 men over 50 years old and is the precursor to fragility fractures.4 In Europe, an estimated 3.79 million fractures occurred in 2000, of which a quarter were hip fractures, costing an estimated €31.7 billion.5 Screening, as well as primary and secondary prevention of fractures, has—through osteoporosis research—been identified as a cost-effective strategy.6 A central unanswered question remains, i.e. does heart failure lead to osteoporosis and frailty fractures, or is it a passive participant in a population at risk for both diseases? Several observations provide insight into this question and partial answers. In an initial description of 101 patients with end-stage heart failure awaiting cardiac transplantation, low bone mass (osteopenia and osteoporosis) was common, as was vitamin D deficiency and hyperparathyroidism.7 In a study of male heart failure patients with serial bone mineral density scans over 2 years, 35% of patients had significant bone loss which was higher than that of normal age-matched controls.8 Furthermore in a population health study of 16 294 patients (of whom 2041 had heart failure and 14 253 had other acute cardiovascular diseases), patients with heart failure had a 4-fold risk for any fracture (and 6-fold for hip fracture) when compared with a non-heart failure cardiovascular control population.9 This excess in fracture risk persisted even after adjustment for most known risk factors for osteoporosis and fragility fractures. Although this study lacked biomarker and functional status assessment, it nonetheless provides a strong epidemiological association deserving confirmation in other populations. Carbone and colleagues have examined the relationship between heart failure, hip fracture, and mortality from the Cardiovascular Health Study (CHS).10 A total of 5613 individuals free from heart failure at the initiation of the CHS were followed for over a decade. Heart failure developed in 27%; 5% of these patients developed a hip fracture (a total of 70 hip fractures) after developing heart failure. This approximates to 14 fractures per 1000 patient-years which was double that of those who never developed heart failure (6.8 fractures per 1000 patient-years) and translates to hazard ratios of 1.87 and 1.75 for men and women, respectively. Adjustment for known risk factors for osteoporosis and hip fracture revealed that patients with heart failure were still more likely (1.6 times for men and 1.4 times for women) than a control population to have a hip fracture. Although the P-values were non-significant (P = 0.09 and 0.06 for men and women, respectively) for the multivariable relationship between heart failure and hip fracture, the association is a clinically significant finding. Caution should be exercised in relying exclusively on the P-value to avoid the ‘P-value fallacy’: ‘the mistaken idea that a single number can capture both the long-run outcomes of an experiment and the evidential meaning of a single result’ without consideration of either the effect size observed or previous estimates of effect.11 The effect size in this study, together with prior research, confirms the association. Whereas the authors deserve commendation for their efforts to delineate the time-course of incident and prevalent heart failure and hip fracture cases, their analysis population includes 89 patients with heart failure who had prior hip fracture and an unknown number in the control population. Since prior hip or fragility fracture strongly predicts future fractures, we are uncertain about the validity of the hazard ratios obtained by their inclusion, given the resultant bias toward the null hypothesis.12 Furthermore, focusing solely on hip fractures, while methodologically appealing, excludes other important fracture sites (especially vertebral13 and wrist6) which represent >50% of all symptomatic fractures.14 What are the possible links between osteoporosis, fractures, and heart failure? (see Figure 1). Many, including Carbone and colleagues, point to shared risk factors for both diseases. While older age, smoking, diabetes, renal dysfunction, inactivity, and poor nutrition are features shared by osteoporosis and heart failure, some features remain unique to heart failure. For example, elevated aldosterone levels—common in heart failure—have been demonstrated to play a role in the risk for orthopaedic fracture in animal models via magnesium and calcium wasting in the urine coupled with secondary hyperparathyroidism.15 Furthermore, in a study of US male heart failure patients, those prescribed spironolactone compared with a matching cohort of heart failure patients had fewer fractures,16 suggesting that aldosterone may be a key determinant of fracture risk. Whether this benefit relates to interference with the aldosterone pathway and a commensurate reduction in secondary hyperparathyroidism, promoting bone repair, mineralization, or retention of minerals required for maintaining bone structure (calcium and magnesium), is unknown. Elevated markers of fibrosis and collagen formation and degradation have been linked to fracture risk, and recently with outcomes for patients with heart failure, including from the same CHS study.17 Finally, β-blockers, thiazide and loop diuretics, angiotensin-converting enzyme inhibitors, and other cardiovascular medications are intimately linked to increased or decreased fracture risk and must be incorporated into any assessment of the association between heart failure and fracture. The paradigm of heart failure, osteoporosis and fragility fractures. Future research should serially examine biomarkers, imaging, and clinical outcomes related to bone health after carefully clinically phenotyping patients with heart failure. More detailed assessments of how cardiovascular medications are related to osteoporosis, and fracture, is needed and may provide additional targets for primary and secondary prevention. In the interim, clinicians should be proactive and institute guideline-endorsed screening measures [such as dual-energy X-ray absorptiometry (DEXA) scans] and prevention strategies (such as vitamin D, calcium, and other agents) for those patients deemed to be at high risk of future fragility fracture. I thank Dr P. W. Armstrong and Dr S. R. Majumdar for review of an earlier draft. J.A.E. was supported by the Canadian Institute of Health Research and the Alberta Heritage Foundation for Medical Research. Conflict of interest: none declared.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0270.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.342
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations9
Published2009
Admission routes2
Has abstractyes

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