Gestational diabetes that is not properly controlled with diet has been commonly treated with insulin. In recent years, several studies have published that metformin can lead to, at least, similar ...obstetrical and perinatal outcomes as insulin. Nevertheless, not all clinical guidelines endorse its use, and clinical practice is heterogeneous.
This study aimed to test whether metformin could achieve the same glycemic control as insulin and similar obstetrical and perinatal results, with a good safety profile, in women with gestational diabetes that is not properly controlled with lifestyle changes.
The metformin for gestational diabetes study was a multicenter, open-label, parallel arms, randomized clinical trial performed at 2 hospitals in Málaga (Spain), enrolling women with gestational diabetes who needed pharmacologic treatment. Women at the age of 18 to 45 years, in the second or third trimesters of pregnancy, were randomized to receive metformin or insulin (detemir or aspart). The main outcomes were (1) glycemic control (mean glycemia, preprandial and postprandial) and hypoglycemic episodes and (2) obstetrical and perinatal outcomes and complications (hypertensive disorders, type of labor, prematurity, macrosomia, large for gestational age, neonatal care unit admissions, respiratory distress syndrome, hypoglycemia, jaundice). Outcomes were analyzed on an intention-to-treat basis.
Between October 2016 and June 2019, 200 women were randomized, 100 to the insulin-treated group and 100 to the metformin-treated group. Mean fasting and postprandial glycemia did not differ between groups, but postprandial glycemia was significantly better after lunch or dinner in the metformin-treated-group. Hypoglycemic episodes were significantly more common in the insulin-treated group (55.9% vs 17.7% on metformin; odds ratio, 6.118; 95% confidence interval, 3.134–11.944; P=.000). Women treated with metformin gained less weight from the enrollment to the prepartum visit (36–37 gestational weeks) (1.35±3.21 vs 3.87±3.50 kg; P=.000). Labor inductions (45.7% metformin vs 62.5% insulin; odds ratio, 0.506; 95% confidence interval, 0.283–0.903; P=.029) and cesarean deliveries (27.6% metformin vs 52.6% insulin; odds ratio, 0.345; 95% confidence interval, 0.187–0.625; P=.001) were significantly lower in the metformin-treated group. Mean birthweight, macrosomia, and large for gestational age and babies’ complications were not different between treatment groups. The lower cesarean delivery rate for women treated with metformin was not associated with macrosomia, large or small for gestational age, or other complications of pregnancy.
Metformin treatment was associated with a better postprandial glycemic control than insulin for some meals, a lower risk of hypoglycemic episodes, less maternal weight gain, and a low rate of failure as an isolated treatment. Most obstetrical and perinatal outcomes were similar between groups.
Metformin, which is known to produce profound changes in gut microbiota, is being increasingly used in gestational diabetes mellitus (GDM). The aim of this study was to elucidate the differences in ...gut microbiota composition and function in women with GDM treated with metformin compared to those treated with insulin.
From May to December 2018, 58 women with GDM were randomized to receive insulin (INS; n = 28) or metformin (MET; n = 30) at the University Hospital Virgen de la Victoria, Málaga, Spain. Basal visits, with at least 1 follow-up visit and prepartum visit, were performed. At the basal and prepartum visits, blood and stool samples were collected. The gut microbiota profile was determined through 16S rRNA analysis.
Compared to INS, women on MET presented a lower mean postprandial glycemia and a lower increase in weight and body mass index (BMI). Firmicutes and Peptostreptococcaceae abundance declined, while Proteobacteria and Enterobacteriaceae abundance increased in the MET group. We found inverse correlations between changes in the abundance of Proteobacteria and mean postprandial glycemia (p = 0.023), as well as between Enterobacteriaceae and a rise in BMI and weight gain (p = 0.031 and p = 0.036, respectively). Regarding the metabolic profile of gut microbiota, predicted metabolic pathways related to propionate degradation and ubiquinol biosynthesis predominated in the MET group.
Metformin in GDM affects the composition and metabolic profile of gut microbiota. These changes could mediate, at least in part, its clinical effects. Studies designed to assess how these changes influence metabolic control during and after pregnancy are necessary.
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•In GDM, metformin increases Proteobacteria and Enterobacteriaceae abundance.•In GDM, metformin decreases Firmicutes and Peptoestreptococcaceae abundance.•Enterobacteriaceae increase was inversely correlated with BMI and weight gain.•Proteobacteria increase was inversely correlated with mean postprandial glycemia.
Gestational diabetes mellitus (GDM) increases the risk of developing metabolic disorders in both pregnant women and their offspring. Factors such as nutrition or the intrauterine environment may play ...an important role, through epigenetic mechanisms, in the development of GDM. The aim of this work is to identify epigenetic marks involved in the mechanisms or pathways related to gestational diabetes. A total of 32 pregnant women were selected, 16 of them with GDM and 16 non-GDM. DNA methylation pattern was obtained from Illumina Methylation Epic BeadChip, from peripheral blood samples at the diagnostic visit (26-28 weeks). Differential methylated positions (DMPs) were extracted using ChAMP and limma package in R 2.9.10, with a threshold of FDR <0.05, deltabeta >|5|% and B >0. A total of 1.141 DMPs were found, and 714 were annotated in genes. A functional analysis was performed, and we found 23 genes significantly related to carbohydrate metabolism. Finally, a total of 27 DMPs were correlated with biochemical variables such as glucose levels at different points of oral glucose tolerance test, fasting glucose, cholesterol, HOMAIR and HbA1c, at different visits during pregnancy and postpartum. Our results show that there is a differentiated methylation pattern between GDM and non-GDM. Furthermore, the genes annotated to the DMPs could be implicated in the development of GDM as well as in alterations in related metabolic variables.
Objective
Obesity‐associated hypoandrogenemia is increasing in parallel to the obesity epidemic. The prevalence of hypoandrogenemia in nondiabetic young men with obesity is not known. This study ...aimed to evaluate the prevalence of hypoandrogenemia and associated risk factors in this population.
Methods
This cross‐sectional study included 266 nondiabetic men < 50 years of age with obesity who were referred from primary care. Total testosterone (high‐performance liquid chromatography mass spectrometry), sex hormone–binding globulin, free testosterone (FT), luteinizing hormone (LH), high‐sensitivity C‐reactive protein, and homeostatic model assessment of insulin resistance were determined. Body composition and erectile function were also assessed. Hypoandrogenemia was defined as FT level < 70 pg/mL.
Results
Subnormal FT concentrations were found in 25.6% of participants. Hypoandrogenemia prevalence was different along the BMI continuum, being > 75% in individuals with BMI ≥ 50 kg/m2. A multivariate regression analysis indicated that increasing BMI (P < 0.001), age (P = 0.049), and reduced LH levels (P = 0.003) were independent risk factors for hypoandrogenemia.
Conclusions
In a primary care–based cohort of nondiabetic young men with obesity, hypoandrogenemia was a very prevalent finding and was directly associated with adiposity. Obesity, age, and reduced LH levels were independent risk factors associated with hypoandrogenemia. Further prospective studies are needed to evaluate the long‐term consequences of hypoandrogenemia in this population.
Gestational diabetes, metformin, and risk of hypoglycemia Picón-César, María J.; Molina-Vega, María; González-Romero, Stella
American journal of obstetrics and gynecology,
September 2021, 2021-09-00, 20210901, Letnik:
225, Številka:
3
Journal Article
Diabetes is a metabolic disorder of glucose homeostasis in which β cell destruction occurs silently and is detected mainly when symptoms appear. In the last few years, it has emerged a great interest ...in developing markers capable of detecting pancreatic β cell death focused on improving early diagnosis and getting a better treatment response, mainly in type 1 diabetes. But other types of diabetes would also benefit from early detection of β cell death. Differentially methylated circulating DNA is being studied as minimally invasive biomarker of cell death. We aimed to explore whether the unmethylated/methylated ratio of the insulin and amylin genes might be a good biomarker of β cell death in different types of diabetes. A lower index ∆Ct indicates a higher rate of β-cell death. Plasma samples from subjects without diabetes, pregnant women, pregnant with gestational diabetes (GDM), type 1 diabetes and type 2 diabetes were analyzed. A qPCR reaction with specific primers for both methylated and unmethylated fragments of insulin and amylin genes were carried out. Pregnant women, GDM and non- GDM, showed a higher β-cell death for both markers (∆INS = 3.8 ± 2.1 and ∆Amylin = 8.5 ± 3.6), whereas T1D presented lower rate (∆INS = 6.2 ± 2.1 and ∆Amylin = 10.7 ± 2.9) comparable to healthy subjects. The insulin methylation index was associated with the newborn birth weight (r = 0.46; p = 0.033) and with insulin resistance (r = -0.533; p = 0.027) in the GDM group. The higher rate of β-cell death was observed in pregnant women independently of their metabolic status. These indexes could be a good indicator of β cell death in processes caused by defects on insulin secretion, insulin action, or both.
Despite bariatric surgery being the most effective treatment for obesity, some individuals do not respond adequately, especially in the long term. Identifying the predictors of correct weight ...maintenance in the medium (from 1 to 3 years after surgery) and long term (from 3 years and above) is of vital importance to reduce failure after bariatric surgery; therefore, we summarize the evidence about certain factors, among which we highlight surgical technique, psychological factors, physical activity, adherence to diet, gastrointestinal hormones or neurological factors related to appetite control. We conducted a search in PubMed focused on the last five years (2015-2021). Main findings are as follows: despite Roux-en-Y gastric bypass being more effective in the long term, sleeve gastrectomy shows a more beneficial effectiveness-complications balance; pre-surgical psychological and behavioral evaluation along with post-surgical treatment improve long-term surgical outcomes; physical activity programs after bariatric surgery, in addition to continuous and comprehensive care interventions regarding diet habits, improve weight loss maintenance, but it is necessary to improve adherence; the impact of bariatric surgery on the gut-brain axis seems to influence weight maintenance. In conclusion, although interesting findings exist, the evidence is contradictory in some places, and long-term clinical trials are necessary to draw more robust conclusions.
Men with obesity tend to be insulin resistant and often have low-normal testosterone concentrations. We conducted a clinical trial aimed to evaluate potential therapeutic strategies for low ...testosterone in men with obesity.
We did a 1-year, parallel, randomized, double-blind, placebo-controlled trial, where we evaluated the independent and combined effects of metformin and testosterone in 106 men with obesity, aged 18–50 years, who had low levels of testosterone and no diabetes mellitus. The primary outcome was change in insulin resistance, measured as Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) index. Secondary outcomes included changes in total and free serum testosterone, body composition, metabolic variables, erectile function, and health-related quality of life (HRQoL).
In the intention-to-treat analysis, the HOMA-IR index decreased significantly in all active groups compared to placebo (metformin −2.4, 95 % CI −4.1 to −0.8, p = 0.004; testosterone −2.7, 95 % CI −4.3 to −1.1, p = 0.001; combination −3.4, 95 % CI −5.0 to −1.8, p < 0.001). Combination therapy was not superior to testosterone alone in decreasing insulin resistance (−0.7, 95 % CI −2.3 to 0.9, p = 0.383). Only the combination of metformin plus testosterone significantly increased total and free testosterone concentrations, compared to placebo. No significant changes in body composition (except for a higher decrease in fat mass in the metformin and combination group), metabolic variables, erectile function, or HRQoL were found with any treatment.
Among men with obesity and low testosterone concentrations, the combination of metformin plus testosterone, metformin only, and testosterone only, compared to placebo, reduced insulin resistance with no evidence of additive benefit.
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•Men with obesity tend to be insulin resistant and often have low-normal testosterone concentrations.•We conducted a randomized, double-blind, placebo-controlled trial of metformin, testosterone, or both, with the endpoint of insulin resistance.•Metformin only, testosterone only, and the combination of metformin plus testosterone reduced insulin resistance with no evidence of additive benefit.
The interaction between genetic susceptibility, epigenetic, endogenous, and environmental factors play a key role in the initiation and progression of autoimmune thyroid diseases (AITDs). Studies ...have shown that gut microbiota alterations take part in the development of autoimmune diseases. We have investigated the possible relationship between gut microbiota composition and the most frequent AITDs. A total of nine Hashimoto's thyroiditis (HT), nine Graves-Basedow's disease (GD), and 11 otherwise healthy donors (HDs) were evaluated. 16S rRNA pyrosequencing and bioinformatics analysis by Quantitative Insights into Microbial Ecology and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) were used to analyze the gut microbiota. Beta diversity analysis showed that gut microbiota from our groups was different. We observed an increase in bacterial richness in HT and a lower evenness in GD in comparison to the HDs. GD showed a significant increase of
,
and
compared to HDs and the core microbiome features showed that
and
characterized this group.
was increased in HT and was part of their core microbiome.
,
and
were greater in HT compared to GD. Core microbiome features of HT were represented by
,
,
,
and
.
decreased in both AITDs compared to HDs. PICRUSt analysis demonstrated enrichment in the xenobiotics degradation, metabolism, and the metabolism of cofactors and vitamins in GD patients compared to HDs. Moreover, correlation studies showed that some bacteria were widely correlated with autoimmunity parameters. A prediction model evaluated a possible relationship between predominant concrete bacteria such as an unclassified genus of
,
and
in AITDs. AITD patients present altered gut microbiota compared to HDs. These alterations could be related to the immune system development in AITD patients and the loss of tolerance to self-antigens.
An adverse intrauterine or periconceptional environment, such as hyperglycemia during pregnancy, can affect the DNA methylation pattern both in mothers and their offspring. In this study, we explored ...the epigenetic profile in maternal peripheral blood samples through pregnancy to find potential epigenetic biomarkers for gestational diabetes mellitus (GDM), as well as candidate genes involved in GDM development. We performed an epigenome-wide association study in maternal peripheral blood samples in 32 pregnant women (16 with GDM and 16 non-GDM) at pregnancy week 24-28 and 36-38. Biochemical, anthropometric, and obstetrical variables were collected from all the participants. The main results were validated in an independent cohort with different ethnic origin (European = 307; South Asians = 165). Two hundred and seventy-two CpGs sites remained significantly different between GDM and non-GDM pregnant women across two time points during pregnancy. The significant CpG sites were related to pathways associated with type I diabetes mellitus, insulin resistance and secretion. Cg01459453 (SELP gene) was the most differentiated in the GDM group versus non-GDM (73.6 vs. 60.9, p = 1.06E-11; FDR = 7.87E-06). Three CpG sites (cg01459453, cg15329406, and cg04095097) were able to discriminate between GDM cases and controls (AUC = 1; p = 1.26E-09). Three differentially methylated positions (DMPs) were replicated in an independent cohort. To conclude, epigenetic marks during pregnancy differed between GDM cases and controls suggesting a role for these genes in GDM development. Three CpGs were able to discriminate GDM and non-GDM groups with high specificity and sensitivity, which may be biomarker candidates for diagnosis or prediction of GDM.
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