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Enregistrement W2731564678 · doi:10.1210/en.2017-00438

Estrogen Deficiency Plus Type 1 Diabetes: A Double-Dip for Bone Loss

2017· letter· en· W2731564678 sur OpenAlexaff
Suresh Mishra, B. L. Grégoire Nyomba

Notice bibliographique

RevueEndocrinology · 2017
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueMetabolism, Diabetes, and Cancer
Établissements canadiensUniversity of Manitoba
Organismes subventionnairesnon disponible
Mots-clésEndocrinologyInternal medicineEstrogenType 2 diabetesMedicineDiabetes mellitus

Résumé

récupéré en direct d'OpenAlex

Type 1 diabetes (T1D) develops subsequent to autoimmune destruction of insulin-producing pancreatic β-cells and is characterized by hypoinsulinemia and hyperglycemia (1). More than one million adult Americans are living with T1D, and roughly half of them are women. This number is predicted to grow four to five times by 2050. T1D-associated annual health care costs in the United States are estimated to be $14 billion and will grow substantially with the continuous increase in the number of people affected with T1D (1). It is well-known that hyperglycemia is a cause of diabetic microvascular and macrovascular complications. Emerging evidence suggests that T1D also has harmful effects on bone, resulting in a decrease in trabecular and cortical bone mass and increase in fracture risk (2). T1D-related decline in bone health is primarily due to impaired development and differentiation of osteoblasts and increased bone inflammation and bone marrow adiposity. The role of osteoclasts in T1D-induced bone loss is inconsistent, with reports showing increase, decrease, or no change in their number or activity (2). Better known than T1D-associated bone loss is postmenopausal osteoporosis, with estrogen deficiency as the canonical cause. A pertinent question then is this: does menopause further alter bone loss in women with T1D to cause harm to the skeleton beyond what is usual in postmenopausal osteoporosis? This is crucial because, since the discovery of insulin, people with T1D are living longer, and the number of women having both T1D and estrogen deficiency is increasing. Although mechanisms of osteoporosis due to estrogen deficiency and T1D have been studied independently, little is known about the impact of concomitant estrogen deficiency and T1D on bone health. Decreased bone mineral density (BMD) is found in postmenopausal women with T1D, and its putative pathogenic mechanisms include oxidative stress, microangiopathy, and deficiencies in hormones such as insulin, insulinlike growth factor 1, and amylin (2). In rats, combined ovariectomy (OVX)-T1D displayed greater reduction of femoral and vertebral BMD vs T1D or OVX alone, with decreased bone formation and increased resorption markers (3), but no underlying mechanisms of the additive effects of OVX and T1D were studied. In the current issue of Endocrinology, Raehtz et al. (4) demonstrate that bone inflammation is the underlying mechanism of the interaction between OVX and T1D worsening bone loss. To reach this conclusion, they carried out a combination of in vivo and in vitro experiments. In vivo, they performed OVX followed after 1 week by T1D induction in female BALB/c mice, and they determined bone parameters 4 weeks later. Microcomputed tomography showed a marked reduction in trabecular bone volume fraction in the OVX-T1D mice (∼82%) that was far greater than that (∼50%) found in either OVX or T1D groups. Osteoblast markers, number, and activity were similarly decreased. Conversely, osteoclast parameters were increased in OVX-T1D vs control mice, although they were not always affected by OVX or T1D alone. More consistently, bone marrow adiposity was increased in OVX-T1D mice vs OVX or T1D mice. As a molecular mechanism for the observed anomalies, the authors determined tumor necrosis factor–α (TNF-α) expression and found it to be insignificantly or slightly increased by OVX or T1D alone but clearly increased in OVX-T1D mice. In addition, TNF-α expression correlated with bone loss and osteoblast and osteocyte death. Next, using in vitro experiments, the authors demonstrated that high glucose in the absence of estradiol enhances TNF-α expression in preosteoblast MC3T3-E1 cells, although high glucose or lack of estrogen alone did not affect TNF-α expression. Together, these findings led the authors to conclude that estrogen deficiency exacerbates T1D-induced bone TNF-α expression, triggering a cascade of events resulting in osteoporosis in female mice. The study by Raehtz et al. (4) establishes hyperglycemia as what conspires with estrogen deficiency to exacerbate bone mass reduction and osteoporosis. In this respect, this study may be at odds with observations that postmenopausal women with T2D have normal or increased bone density although they have increased fracture risk, likely due to altered bone quality (2). This suggests that hyperglycemia may not be the only factor interacting with estrogen deficiency to reduce bone mass in postmenopausal women with T1D. Deficiencies in insulin, amylin, and insulinlike growth factor 1, as well as oxidative stress and immune alterations, could also be involved (2). The study definitely supports clinical observations that postmenopausal women with T1D have decreased BMD (2) and has the additional merit of proposing a mechanistic interactive link between menopause and T1D. However, OVX followed by T1D induction models premature ovarian failure followed by T1D more so than it models T1D followed by menopause, which is the rationale of this study. Therefore, the content of this article would not change if the title read “Type 1 Diabetes Exacerbates Estrogen Deficiency–Induced Bone TNF-α Expression and Osteoporosis in Female Mice.” It should also be emphasized that menopause is not simply a state of estrogen deficiency but involves a gradual decline in follicle number, decreasing rates of ovulation, hormonal changes, and increasing bone loss that predate menopause by several years (5). In addition, menopause-related gradual deterioration in bone structure is characterized by a distinct pattern of bone remodeling (6). During the initial years after menopause, the decline in estrogen production results in increased bone remodeling, where both bone formation and resorption are increased. Subsequently, these processes are no longer balanced, and bone resorption outweighs bone formation, leading to net bone loss and osteoporosis (6). These features of menopause are not mimicked in the surgical OVX model with its rapid loss of estrogen. Besides estrogens, other ovarian hormones are also lost with OVX and menopause and could be instrumental in postmenopausal bone loss. Recent evidence suggests that progesterone, for example, influences the dynamics of bone remodeling through its action on osteoblasts and osteoclasts (5). A pilot study in perimenopausal women suggested that women with higher progesterone levels, seen in the luteal phase of ovarian cycles, enjoy more bone formation and slightly less resorption than those with anovulatory cycles and low progesterone levels (5). Moreover, progesterone modulates immune/inflammatory functions through regulation of macrophage and dendritic cell functions (7), which share a common progenitor with osteoclasts. Thus, progesterone may play an important role in the interrelationship between ovaries and bone health. In sum, existing data emphasize the complexity of the interactions between ovarian steroids, body metabolism, and inflammation in bone remodeling, health, and disease. Moving forward, much remains to be understood on how menopause exacerbates bone loss in T1D. Future investigations using hormone replacement therapy and progesterone assessment in animal models and perimenopausal women who are estrogen-replete may shed new light on the relationship between ovarian steroid hormones, diabetes, and immune dysregulation in bone physiology and disease. bone mineral density ovariectomy type 1 diabetes tumor necrosis factor. This work was supported by Natural Sciences and Engineering Research Council of Canada Grant RGPIN-2017-04962 and Research Manitoba. Disclosure Summary: The authors have nothing to disclose.

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)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,010

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0100,009
Charge utile insuffisante (le modèle a refusé de juger)0,0030,002

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 Opus0,027
Tête enseignante GPT0,287
Écart entre enseignants0,259 · 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'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations2
Publié2017
Routes d'admission1
Résumé présentnon

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