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BACKGROUND: Microbiome therapies (probiotic, prebiotic, and synbiotics) have been proposed as adjuvants in the control of central obesity; however, their results for patients with type 2 diabetes (T2D) remain inconclusive. OBJECTIVE: The aim of this systematic review and meta-analysis was to evaluate the effect of microbiome therapies on central obesity as measured by waist circumference (WC), and to evaluate the effect of microbiome therapies for glycemic parameters (fasting glucose [FPG], fasting insulin [FPI], hemoglobin A1c [HbA1c], and insulin resistance [HOMA1-IR]) in patients with T2D. METHODS: SCOPUS, Pubmed, EBSCO, and LILACS databases were searched for studies that investigated the effect of microbiome therapies on WC up to June 1, 2022. Heterogeneity was determined using Cochran's Q test and quantified using the inconsistency index. The random effects model was used to calculate the pooled difference in means (DM) and 95% confidence intervals (95%CI). Egger's test and Beggs-Muzamar's test were used to assess publication bias. RESULTS: Fifteen reports were included (443 treated and 387 controls). Overall, a significant decrease in WC was found (DM = -0.97 cm; 95% confidence interval [95%CI] = -1.74 to -0.20; P = 0.014); however, when stratified by type of microbiome therapy, only probiotics significantly decreased WC (DM = -0.62 cm; 95%CI = -1.00 to -0.24; P = 0.002). No effect was observed for prebiotics and synbiotics. With respect to glycemic parameters, HbA1c, FPG, and HOMA1-IR significantly decrease with microbiome therapies (P ≤ 0.001). When stratified by the type of therapy, for probiotic treatments, HbA1c, FPG, and HOMA1-IR scores decrease (P < 0.001). For prebiotic treatments, HbA1c and FPG (P ≤ 0.001) levels decrease, whereas FPI increased (P = 0.012). Synbiotic treatments were only associated with an increase in FPI (P = 0.031). CONCLUSION: Findings indicate that using probiotics alone improved WC in patients with T2D. Both probiotics and prebiotics decreased HbA1c and FPG; however, prebiotics and synbiotics resulted in an increase in FPI. The formulation of the therapy (single vs multi) had no difference on the effect.

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OBJECTIVE: Receptor activator of NF-κß ligand (RANKL) is crucial for the development of hepatic insulin resistance and poor glucose uptake; therefore, inhibiting RANKL with Denosumab could improve fasting plasma glucose (FPG) and insulin (FPI). METHODS: A systematic review was conducted to evaluate the effects of Denosumab on glycemic parameters. PubMed, SCOPUS, EBSCO, and LILACS databases were searched for studies that investigated the effect of Denosumab on FPG, glycated hemoglobin (HbA1c), FPI, and Homeostatic Model Assessment for Insulin Resistance (HOMA1-IR). The pooled standard difference in means (SDM) and 95% confidence intervals (95%CI) were calculated. The results were stratified into (1) Normal Glucose Tolerance (NGT) and (2) Impaired Glucose Tolerance (IGT). RESULTS: Six publications (1203 participants) were included. There was a significant association between Denosumab and FPG (SDM = -0.388, 95%CI: -0.705 to -0.070, p = .017) and with HOMA1-IR (SDM = -0.223, 95%CI: -0.388 to -0.058, p = .008), but not for HbA1c and FPI. When stratified by glucose tolerance, the association between Denosumab and FPG, HbA1c, and HOMA1-IR was present for the IGT group. Lastly, Denosumab had a time-dependent effect on HbA1c (slope = -0.037, 95%CI: -0.059 to -0.015, p < .005). CONCLUSIONS: Denosumab significantly improved glycemic parameters. This outcome was more prominent for subjects with compromised glucose tolerance, positing that Denosumab can be used as a treatment to improve glucose metabolism for persons with pre-diabetes and diabetes.

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BACKGROUND: The metabolic syndrome is a worldwide health issue, with non-alcoholic fatty liver disease (liver steatosis) being one of its features, particularly closely related to insulin resistance. This study aims to investigate whether quantification of hepatic steatosis by abdominal ultrasonography, using hepatorenal index, is a feasible tool for the prediction of insulin resistance, and thus the metabolic syndrome. METHODS: Patients were recruited from the Centre of Obesity at the University Hospital of North Norway, and among participants from the Sixth Tromsø Study. Homeostasis Model Assessment of Insulin Resistance (HOMA1-IR) was measured, body mass index (BMI, kg/m(2)) calculated, and hepatorenal index (HRI), i.e. the ratio of liver to kidney optical densities, was measured by transabdominal ultrasonography. Receiver operating characteristic (ROC) analyses were performed, detecting insulin resistance at HOMA1-IR cut-off values >2.3 and >2.5. RESULTS: Ninety participants were included in the study, of which 46 (51 %) had BMI ≥30 and 27 (30 %) had BMI ≥35. Overall, HRI at level 1.17 had sensitivity 0.90 and specificity 0.70 for predicting insulin resistance (HOMA1-IR >2.3) in all participants. For participants with BMI ≥30, HRI at level 1.17 had sensitivity 0.94 and specificity 0.70, and for BMI ≥35, HRI at level 1.17 had sensitivity 0.93 and specificity 0.75 for predicting HOMA1-IR >2.3. Setting the HRI limit at 1.42 gave low sensitivities and high specificities in all BMI groups. In the analysis predicting HOMA1-IR >2.5, sensitivity values were almost the same as in the analysis predicting HOMA1-IR >2.3, but specificity values were lower in this analysis. CONCLUSION: Detection and quantification of hepatic steatosis by ultrasound and the hepatorenal index is a feasible screening tool for identifying patients with low risk of having insulin resistance (IR, HRI <1.17), patients at risk of having IR (HRI 1.17-1.41) and patients with likely IR (HRI ≥1.42), especially in obese individuals.

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AIMS/HYPOTHESIS: The incidence of type 1 diabetes is increasing at a rate of 3-5% per year. Genetics cannot fully account for this trend, suggesting an influence of environmental factors. The accelerator hypothesis proposes an effect of metabolic factors on type 1 diabetes risk. To test this in the TrialNet Pathway to Prevention (PTP) cohort, we analysed the influence of BMI, weight status and insulin resistance on progression from single to multiple islet autoantibodies (Aab) and progression from normoglycaemia to diabetes. METHODS: HOMA1-IR was used to estimate insulin resistance in Aab-positive PTP participants. Cox proportional hazards models were used to evaluate the effects of BMI, BMI percentile (BMI%), weight status and HOMA1-IR on the progression of autoimmunity or the development of diabetes. RESULTS: Data from 1,310 single and 1,897 multiple Aab-positive PTP participants were included. We found no significant relationships between BMI, BMI%, weight status or HOMA1-IR and the progression from one to multiple Aabs. Similarly, among all Aab-positive participants, no significant relationships were found between BMI, weight status or HOMA1-IR and progression to diabetes. Diabetes risk was modestly increased with increasing BMI% among the entire cohort, in obese participants 13-20 years of age and with increasing HOMA1-IR in adult Aab-positive participants. CONCLUSIONS/INTERPRETATION: Analysis of the accelerator hypothesis in the TrialNet PTP cohort does not suggest a broad influence of metabolic variables on diabetes risk. Efforts to identify other potentially modifiable environmental factors should continue.

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BACKGROUND: Purpose of insulin resistance (IR) adapted by mother is to deliver enough quantity of nutrients to the growing fetus. Many maternal hormones and factors play role in causation of IR during pregnancy. AIM: The study aims at evaluating IR at different trimesters of pregnancy. MATERIALS AND METHODS: Pregnant women at 1(st), 2(nd) and 3(rd) trimester were grouped into groups I, II and III respectively (n=20 in each group). Healthy non-pregnant women were taken as controls (n=30). Fasting plasma glucose (FPG) and fasting serum insulin (FSI) were measured and IR indices such as fasting glucose to insulin ratio (FGIR), quantitative insulin sensitivity check index (QUICKI), log FSI and log HOMA1-IR were calculated. The student's t-test and one way Analysis of variance (ANOVA) were used for data analysis. RESULTS: The mean FSI, log FSI and log HOMA 1-IR were significantly higher in 2(nd) and 3(rd) trimesters while QUICKI was significantly lower in 2(nd) and 3(rd) trimesters of pregnancy when compared with controls. Also, mean FGIR was found to be significantly lower in 3rd trimester when compared with controls. CONCLUSION: As pregnancy advances, IR increases. Increased IR is associated with poor maternal and fetal outcome. Screening of all pregnancy for IR and early intervention may help to reduce the associated complications.