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Cholesterol and Osteoporosis in Postmenopausal Women: A Pilot Study

2003· letter· en· W1493922272 on OpenAlexaffabout
Kannayiram Alagiakrishnan, Laurie Mereu, Ross T. Tsuyuki, Michal S. Kalisiak, Anne Sclater, Marilou Hervas‐Malo

Bibliographic record

VenueJournal of the American Geriatrics Society · 2003
Typeletter
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineOsteoporosisBone mineralInternal medicineBone densityCholesterolPopulationMenopauseIncidence (geometry)EndocrinologyPhysical therapyPhysiologyEnvironmental health

Abstract

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To the Editor: Osteoporosis is a major public health problem in the aging population. It is the main cause of bone fractures in postmenopausal women and the elderly, causing deformity, pain, and loss of independence. It is the most common type of metabolic bone disease, affecting one in four women and one in eight men aged 50 and older. Thirty percent of postmenopausal women sustain an osteoporotic fracture during their lifetime. Because of the exponential increase in fracture incidence after age 75, even interventions that slightly reduce rate of bone loss (from 1.0% to 0.5% per year, for example) are capable of greatly reducing fracture risk. In view of the increase in the aging population and the resultant rise in the prevalence of osteoporosis, the need for focused preventive strategies should become a major public health priority. One study showed that plasma leptin levels but not percentage fat were associated with bone mineral density (BMD) and the presence of vertebral fractures in postmenopausal women.1 Another study pointed out that low-density and high-density lipoprotein cholesterol were inversely and positively correlated with vertebral fractures in postmenopausal women.2 Studies have shown that inhibitors of 3-hydroxy-3-methylglutaril coenzyme A (statins) increase BMD by blocking the cholesterol biosynthetic pathway. Researchers have also shown that lipid-lowering agents such as statins increase BMD and reduce fracture rate,3–5 but this is not a consistent finding, with some studies showing no benefit from statins.6 It was reported in a recent study that diabetic men using statins have higher bone density than diabetic men not requiring this therapy, but no significant effect was found in diabetic women.7 In the study of the effect of pravastatin on frequency of fractures, fracture prevention was not shown.8 Aminobisphosphonates, which are used in the treatment of osteoporosis, are potent antiresorptive agents that cause osteoclast apoptosis, which they achieve by inhibiting the farnesyl diphosphate synthase enzyme in the mevalonate pathway, which is also involved in the synthesis of cholesterol.9 Statins decrease cholesterol synthesis by inhibiting the first step in the same biochemical pathway affected by aminobisphosphonates, and this is their currently proposed mode of action on the bone. Recent research also suggests that statin users have a 60% reduction in fracture risk, which is greater than what would be expected from increased BMD alone.10 It is not clear at this point whether high cholesterol is contributing to the cause of osteoporosis. Our hypothesis is that elevated cholesterol is associated with the pathogenesis of osteoporosis on a vascular basis similar to that of atherosclerosis. The objective of this study was to determine the association between serum cholesterol levels and osteopenia/osteoporosis. We used retrospective chart review of 42 consecutive subjects seen in an endocrinology clinic at the University of Alberta. Using a standardized data collection form, demographic information, details about BMD and severity of osteoporosis (osteopenia, mild osteoporosis, and severe osteoporosis), and total cholesterol level data were collected from the charts. BMD was measured using dual-energy x-ray absorptiometry (DEXA). DEXA values were reported by comparison to age and sex reference groups with t scores (standard deviation or percentage above or below values for young normal subjects) and z scores (standard deviations or percentage above or below age-matched controls). We used World Health Organization definitions, t score better than −1.0 as normal, between −1.0 and −2.5 as osteopenia, less than −2.5 as osteoporosis, and less than −2.5 in presence of an osteoporotic fracture as severe osteoporosis. The average age±standard deviation of the patients was 63±13. All were postmenopausal women, and none were on cholesterol-reducing medications. Patients were classified into groups as presented in Table 1. There were 39 subjects with osteopenia/osteoporosis and three subjects with normal BMD. Twenty-three of 39 subjects in the osteopenia/osteoporosis group (59%) had high cholesterol levels (>5.2 mmol/L), whereas one of three in the normal BMD had high cholesterol level (33%). This difference was not statistically significant (P=.56; odds ratio=2.0, 95% confidence interval=0.2–34.5). Due to small sample size, we did not have the power to detect the relationship. In this pilot study, it was observed that subjects with osteopenia or osteoporosis at lumbar spine and hip were more likely to have elevated cholesterol. This needs to be confirmed with a larger study. If it is proven, aggressive control of elevated cholesterol in addition to the existing therapies may help to reduce the morbidity and mortality associated with osteoporosis.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.307
Teacher spread0.283 · 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 designObservational
Domainnot available
GenreEmpirical

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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Citations5
Published2003
Admission routes2
Has abstractyes

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