Gestational diabetes mellitus: a (nearly) perfect mouse model
Notice bibliographique
Résumé
Diabetes mellitus (DM) is a massive global problem with projected global economic impact, including direct and indirect productivity costs, increasing from $1.3 trillion USD to approximately $2.2 trillion USD by 2030 despite international interventions (Bommer et al. 2018). In addition, a massive increase in prevalence was predicted across most age groups, from 8.8% to 10% in the baseline scenario. Included in these values is a subtype of DM, gestational diabetes mellitus (GDM), characterized by onset of hyperglycaemia only in the gestational period. According to the WHO in 2013, the global prevalence of GDM is nearly 17% in pregnant female cohorts when diagnostic thresholds set by the International Association of the Diabetes and Pregnancy Study Groups are applied (Feig et al. 2018) and increasing due to a variety of reasons, including the increasing incidence of often comorbid obesity. As pregnancy progresses, the surge of pregnancy hormones including prolactin, progesterone and placental lactogen promote a temporary state of mild insulin resistance, allowing blood glucose to remain high and supply the fetus via the placenta (Plows et al. 2018). Maternal placental hormones then trigger a response in pancreatic β-cells to attempt to restore euglycaemia – β-cells and often α-cells of the pancreas undergo hypertrophy and hyperplasia. In a recent issue of The Journal of Physiology, Szlapinski and colleagues demonstrated that pregnant mice classified as having GDM displayed pancreata with significantly decreased α- and β-cell mass, corresponding to an overall decrease in insulin production and secretion, and therefore a globally reduced glucose sensitivity (Szlapinski et al. 2019). This pathology represents approximately 80% of GDM cases, which are classified based on β-cell dysfunction with previous history or worsening peripheral insulin resistance (Plows et al. 2018), indicating a diabetic disorder that includes aspects of all three major categories of the disease (Type I, Type II and other). Since early treatment of GDM can reduce the incidence of pre-eclampsia, fetal overgrowth and more in the fetus (Feig et al. 2018), accurate diagnosis and adequate treatment is paramount to the health of the child. Though the pathophysiology of GDM is relatively well understood, few effective preventative treatments and measures work. Live human models of GDM, though, can be difficult to study, with both physical and ethical barriers that come with placental sampling. Previous models in rodents have induced GDM with low doses of streptozotocin (STZ) in combination with high sucrose and fat diets among other factors (Aziz et al. 2016). While these models achieved a snapshot of GDM, they did not demonstrate the progressive glucose intolerance typical of GDM. Szlapinski and colleagues targeted this issue in their study. They aimed to develop a diet-induced progressive mouse model of gestational glucose intolerance, whereby the intolerance mirrored human gestational diabetes. This generational model was successfully accomplished through timed pregnancies by mating male and female mice, alongside the establishment of mouse oestrous cycling. After female pregnant mice were housed separately, Szlapinski and colleagues randomly divided this group (F0) into two isocaloric diet groups: a control (C) group, which consisted of 24 dams that were fed a 20% protein diet, and a group of 21 dams that were fed a low protein (LP) diet, which consisted of 8% protein. Female offspring (F1) were then separated into pregnant and non-pregnant groups. In order to create an animal model of GDM, at maturity, F1 females were mated with males on a control diet. In order to test glucose tolerance, F0 and F1 generation mice underwent an intraperitoneal glucose tolerance test at separate times, according to the previously described techniques. To localize and quantify insulin for immunohistochemistry, insulin-expressing cells were colocalized with the proliferative marker Ki-67, allowing accurate identification of β-cells and their proliferative rate. The quantification of β-cells thus allowed the definition and quantification of pancreatic islets, which served as a basis for comparing β-cell growth and apoptosis. In the F0 mice, Szlapinski and colleagues showed that there were no noticeable differences between the C and the LP diet treatment groups. Maternal weight gain and litter size was consistent and an intraperitoneal glucose tolerance test (IPGTT) was performed at 1 month postpartum which indicated that there was no difference in glucose tolerance across the two groups. However, in the F1 generation, the offspring born to LP diet dams weighed less at birth compared to the C diet offspring (1.25 ± 0.02 g vs. 1.34 ± 0.03 g, P < 0.05). The offspring of LP diet mice continued to weigh less throughout life. The weight difference was particularly apparent during late gestation where low protein pregnant (LPP) females weighed significantly less than the control pregnant (CP) females ((12.78 ± 1.22 g vs. 15.24 ± 1.44 g, P < 0.001). During pregnancy the LPP and the CP groups showed no differences in fetal resorption. Fasting blood glucose levels in LPP and CP females demonstrated no significant differences but by gestational day (GD) 18, the LPP females had significantly higher non-fasting blood glucose compared to CP mice. The β-cell mass (BCM) in LPP females was also drastically lower than the expansion of BCM in CP females, indicating that changes in the pancreas contribute to glucose intolerance. Szlapinski and colleagues were able to determine that the reduced BCM was caused by reduced proliferation of β-cells in LPP females. The mean islet size was reduced in the LPP females at GD18 even though there was no difference in β-cell size. However, the number of small islets in LPP compared to CP females was reduced. In the presence of low glucose (2.8 mmol/L), both the LPP and the CP islets at GD18 showed similar insulin secretion. However, when subjected to high glucose (16.7 mmol/L), the LPP islets had much lower insulin secretion. Szlapinski et al. demonstrated a thorough methodology, although an area of potential improvement was identified in the quantification of islets. In rodent pancreata, β-cells make up 70–80% of a typical islet, with islet size ranging from 10 to 10,000 cells (Jo et al. 2007). The lowest end of this range, 10 cells, with the lower range of β-cell composition, 70%, suggests that the smallest potential grouping of β-cells that would constitute an islet is seven β-cells. Szlapinski et al. used a threshold of six β-cells to define their search for islets, following the threshold of articles by Beamish et al. (2016, 2017) cited by Szlapinski et al. Neither of these articles includes a concrete evaluation in developing the threshold number, suggesting that an arbitrary value was chosen. That said, setting the threshold for six or higher includes a broader range of large pancreatic islets as well as small clusters for data evaluation, leading to a potentially more thorough analysis. A progressive model of GDM as developed by Szlapinski and colleagues reveals numerous opportunities for research on prevention and treatment of GDM, which subsequently could aid in decreasing a massive healthcare burden, as women with GDM face increased risk of dysglycaemia postpartum, a high recurrence rate in subsequent pregnancies, and increased risk of metabolic syndrome (Feig et al. 2018). Additionally, improved insight to the disease may reduce the incidence of the previously mentioned obstetric complications. Benefits aside, there are some limitations that we must consider. Firstly, there are various presentations of GDM. While this model represents the vast majority of cases, other pathophysiological circumstances may gain no benefit from this model, thus alluding to a need for future work (Feig et al. 2018). Additionally, although diet alone is the only factor that can increase/decrease the risk of developing GDM in this model, there are other lifestyle factors that should be included in a model, such as exercise and protein source. Feig and colleagues showed that a low carbohydrate and high animal protein diet leads to an increased risk of T2DM after GDM; however, similar high vegetable protein diets showed a slightly decreased risk. This article further supports Szlapinski et al.’s claims that increased carbohydrates were not the likely culprits for dysglycaemia by outlining results showing that only low-glycaemic-index diets, as opposed to low-carbohydrate diets, have any effect in restoring euglycaemia. All in all, the model developed by Szlapinski et al. really is a (nearly) perfect mouse model of GDM. None declared. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. No funding was received for this work.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,005 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».