ObjectiveThe gut microbiota is known to be related to type 2 diabetes (T2D), psychiatric conditions, and opioid use. In this study, we tested the hypothesis that variability in gut microbiota in T2D is associated with psycho-metabolic health.MethodsA cross-sectional study was conducted among African American men (AAM) (n = 99) that were outpatients at a Chicago VA Medical Center. The main outcome measures included fecal microbiota ecology (by 16S rRNA gene sequencing), psychiatric disorders including opioid use, and circulating leptin and oxytocin as representative hormone biomarkers for obesity and psychological pro-social behavior.ResultsThe study subjects had prevalent overweight/obesity (78%), T2D (50%) and co-morbid psychiatric (65%) and opioid use (45%) disorders. In the analysis of microbiota, the data showed interactions of opioids, T2D and metformin with Bifidobacterium and Prevotella genera. The differential analysis of Bifidobacterium stratified by opioids, T2D and metformin, showed significant interactions among these factors indicating that the effect of one factor was changed by the other (FDR-adjusted p [q] < 0.01). In addition, the pair-wise comparison showed that participants with T2D not taking metformin had a significant 6.74 log2 fold increase in Bifidobacterium in opioid users as compared to non-users (q = 2.2 x 10−8). Since metformin was not included in this pair-wise comparison, the significant ‘q’ suggested association of opioid use with Bifidobacterium abundance. The differences in Bifidobacterium abundance could possibly be explained by opioids acting as organic cation transporter 1 (OCT1) inhibitors. Analysis stratified by lower and higher leptin and oxytocin (divided by the 50th percentile) in the subgroup without T2D showed lower Dialister in High-Leptin vs. Low-Leptin (p = 0.03). Contrary, the opposite was shown for oxytocin, higher Dialister in High-Oxytocin vs. Low-Oxytocin (p = 0.04).ConclusionsThe study demonstrated for the first time that Bifidobacterium and Prevotella abundance was affected by interactions of T2D, metformin and opioid use. Also, in subjects without T2D Dialister abundance varied according to circulating leptin and oxytocin.

Objective This double blind, randomized, controlled trial evaluated 12 months high dose vitamin D2 supplementation for improving insulin sensitivity, secretion and glycemic status. Methods African American men with prediabetes (A1C 5.7 – 6.4%), hypovitaminosis D (25OHD 5 – 29 ng/ml), and prevalent medical problems were supplemented with vitamin D3 (400 IU/day) and then randomized to weekly placebo or vitamin D2 (50,000 IU). The primary outcome was the change in oral glucose insulin sensitivity (OGIS, from oral glucose tolerance test) after 12 months of treatment. Secondary outcomes included other glycemic indices, A1C and incident diabetes. Results Baseline characteristics were similar in vitamin D-supplemented (n = 87) and placebo (n = 86) subjects completing the trial with average concentrations 14.4 ng/ml, 362 and 6.1% for 25OHD, OGIS and A1C, respectively. After 12 months vitamin D-supplemented group had a change in serum 25OHD +35 vs +6 ng/ml for placebo, p<0.001; OGIS +7.8 vs −16.0 for placebo, p = 0.026; and A1C −0.01 vs +0.01% for placebo, p = 0.66; while 10% in both groups progressed to diabetes. A post hoc analysis of participants with baseline impaired fasting glucose showed that more subjects in the vitamin D subgroup (31.6%) than placebo (8.3%) returned to normal glucose tolerance, but the difference did not reach significance (p=0.13). Conclusion The trial does not provide evidence that 12 months of high-dose D2 repletion improves clinically relevant glycemic outcomes in subjects with prediabetes and hypovitaminosis D (NCT01375660).

ObjectiveRecently, it has been suggested that oxytocin (OT) has a role in metabolism and neuropsychiatry health and disease, and therefore, it may represent a potential therapeutic target. The current study aimed to investigate relationships between OT and glycemic status along with psycho-social and behavioral factors. Design and methodsA total of 92 obese or overweight, African American, male subjects were enrolled in the study. Biometric and biochemical data were collected including oral glucose tolerance testing and urinary OT (measured by ELISA). Subjects also completed questionnaires on social and lifestyle factors. ResultsOT levels were found to be significantly lower in subjects with type 2 diabetes mellitus (T2DM) compared to normal glucose tolerance (p<0.05). When stratified by OT tertiles, subjects with higher OT had lower weight, body mass index (BMI) and hemoglobin A1c, but higher eGFR which remained significant after BMI adjustment. The highest OT tertile also had more smokers and more users of psychiatric medications. A stepwise ordered logistic regression supported these findings and could account for 21% of the variation in OT categories (pseudoR 2 = 0.21).

Purpose: Obesity is a chronic disease that is acquiring pandemic proportions. Emerging research suggests that probiotics can be a valuable yet still an underutilized modality for obesity treatment. This review aims to analyze and summarize recent data focusing on published meta-analyses of randomized controlled trials (RCTs) to help understand the role of probiotics in fighting obesity. Materials and Methods: Meta-analyses were sought and reviewed from PubMed, Cochrane Central Library, ScienceDirect, and Google Scholar for body weight and/or BMI changes (two main outcomes of interest). Results: The literature review identified 14 meta-analyses. On average, the meta-analyses dedicated to probiotics included 4-15 trials with 154-994 participants, whereas more inclusive probiotics and/or synbiotics analyses included 15-68 trials with 895-4015 participants. Eleven out of 14 meta-analyses showed that probiotic use in RCTs resulted in reduced body weight and/or BMI compared to placebo. An average weight loss was 0.6 kg, and the most substantial loss was 4.8 kg corresponding to 0.7% and 5.9% reductions in body weight, respectively. Probiotics' use was associated with improved health outcomes in addition to weight loss and was safe. The subgroup analyses showed that the probiotic forms (supplements vs food) and the dosages (lower vs higher than 10 10 CFU/day) did not substantially influence weight loss. The single species particularly helpful for weight loss appeared to be L. gasseri, L. casei, L. delbrueckii, L. reuteri, L. rhamnosus, a combination of L. curvatus and L. plantarum and Bifidobacterium longum. Bacillus subtilis and Akkermansia muciniphila also had a potential as anti-obesity probiotics. Conclusion: Probiotics, despite small effects, could be a valuable addition to the armamentarium of obesity management. Further basic and translational research and clinical trials are required to elucidate mechanisms and specific probiotic and patients' types for the best achievable precision medicine approach to the obesity epidemic.

Background: Adipocytokines are important in type 2 diabetes (T2D). This study explored adipocytokine associations with acute and chronic hyperglycemia in African American Men (AAM). Methods: Fourteen adipocytokines were measured (multiplex assay) in blood samples from men with normal glucose tolerance (NGT) or T2D (drug-naïve MF(-) or using metformin MF(+)). Acute and chronic hyperglycemia were evaluated at 120-minute of OGTT and by HbA1c, respectively. Results: AAM with T2D (N=21) compared to NGT (N=20) were significantly older (59 vs. 54 years) and had higher body mass index (BMI 35 vs. 27 kg/m2) (p<0.01 for both). Fasting and 120-minute OGTT glucose and insulin were higher in T2D than NGT, however, differences did not reach statistical significance after adjusting for age and BMI. In the fasted state, TNF-a, IL-6, PAI-1 (p<0.01 for all) and IL-13, adiponectin, adipsin, lipocalin (p<0.05 for all) were lower in T2D compared to NGT. At 120-minute post-glucose load (acute hyperglycemia) TNF-a, IL-6, IL-13, IL-8, PAI-1, adiponectin, adipsin (p<0.01 for all) and lipocalin, resistin (p<0.05 for both) were lower in T2D than in NGT group. There were no statistical differences for the other adipocytokines including GM-CSF, IL-7, IL-10, IP-10, and MCP-1. Regression analysis (adjusted for age and BMI) showed that fasting IL-8, TNF-a, adiponectin, lipocalin, resistin, adipsin, and PAI-1 were all associated with HbA1c (p<0.05 for all). Further modeling revealed that after adjusting for age, BMI, glucose tolerance status and metformin use, only adipsin remained significantly associated with HbA1c (p=0.004). The model including adipsin, TNF-a, age, BMI, and group designation (i.e. NGT, MF(-), MF(+)) explained 86% of HbA1c variability. Conclusions: The study suggested that adipsin could be independently associated with HbA1c in AAM with varied glucose tolerance. Additional studies should corroborate these data and provide mechanistic insights for enabling adipsin-related discoveries of novel T2D treatment.

This study explored adipocytokine associations with acute and chronic hyperglycemia in African-American men (AAM). Fourteen adipocytokines were measured from men with normal glucose tolerance (NGT) or type 2 diabetes (T2D, drug-naïve MF(−) or using metformin MF(+)). Acute and chronic hyperglycemia were evaluated by 120 min oral glucose tolerance test (OGTT) and glycohemoglobin A1c (HbA1c). AAM with T2D (n = 21) compared to NGT (n = 20) were older, had higher BMI and slightly higher glucose and insulin. In the fasted state, TNF-α, IL-6, PAI-1, IL-13, adiponectin, adipsin, and lipocalin were lower in T2D vs. NGT. At 120 min post-glucose load, TNF-α, IL-6, IL-13, IL-8, PAI-1, adiponectin, adipsin, lipocalin, and resistin were lower in T2D vs. NGT. There were no statistical differences for GM-CSF, IL-7, IL-10, IP-10, and MCP-1. Regression analysis showed that fasting IL-8, TNF-α, adiponectin, lipocalin, resistin, adipsin, and PAI-1 were associated with HbA1c. After adjusting for age, BMI, glucose tolerance, and metformin use, only adipsin remained significantly associated with HbA1c (p = 0.021). The model including adipsin, TNF-α, age, BMI, and group designation (i.e., NGT, MF(−), MF(+)) explained 86% of HbA1c variability. The data suggested that adipsin could be associated with HbA1c in AAM with varied glucose tolerance.