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Enregistrement W1493922272 · doi:10.1046/j.1532-5415.2003.514847.x

Cholesterol and Osteoporosis in Postmenopausal Women: A Pilot Study

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

Notice bibliographique

RevueJournal of the American Geriatrics Society · 2003
Typeletter
Langueen
DomaineMedicine
ThématiqueBone health and osteoporosis research
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésMedicineOsteoporosisBone mineralInternal medicineBone densityCholesterolPopulationMenopauseIncidence (geometry)EndocrinologyPhysical therapyPhysiologyEnvironmental health

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,017

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,007
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0020,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0030,002
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,024
Tête enseignante GPT0,307
Écart entre enseignants0,283 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2003
Routes d'admission2
Résumé présentoui

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