Autosomal dominant hypercholesterolemia in Catalonia: Correspondence between clinical-biochemical and genetic diagnostics in 967 patients studied in a multicenter clinical setting.

BACKGROUND: Autosomal dominant hypercholesterolemia (ADH) is associated with mutations in the low-density lipoprotein (LDL) receptor (LDLR), apolipoprotein B (APOB), and proprotein convertase subtilisin/kexin 9 (PCSK9) genes, and it is estimated to be greatly underdiagnosed. The most cost-effective strategy for increasing ADH diagnosis is a cascade screening from mutation-positive probands. OBJECTIVE: The objective of this study was to evaluate the results from 2008 to 2016 of ADH genetic analysis performed in our clinical laboratory, serving most lipid units of Catalonia, a Spanish region with approximately 7.5 million inhabitants. METHODS: After the application of the Dutch Lipid Clinic Network (DLCN) clinical diagnostic score for ADH, this information and blood or saliva from 23 different lipid clinic units were investigated in our laboratory. DNA was screened for mutations in LDLR, APOB, and PCSK9, using the DNA-array LIPOchip, the next-generation sequencing SEQPRO LIPO RS platform, and multiplex ligation-dependent probe amplification (MLPA). The Simon Broome Register Group (SBRG) criteria was calculated and analyzed for comparative purposes. RESULTS: A total of 967 unrelated samples were analyzed. From this, 158 pathogenic variants were detected in 356 patients. The main components of the DLCN criteria associated with the presence of mutation were plasma LDL cholesterol (LDLc), age, and the presence of tendinous xanthomata. The contribution of family history to the diagnosis was lower than in other studies. DLCN and SBRG were similarly useful for predicting the presence of mutation. CONCLUSION: In a real clinical practice, multicenter setting in Catalonia, the percentage of positive genetic diagnosis in patients potentially affected by ADH was 38.6%. The DLCN showed a relatively low capacity to predict mutation detection but a higher one for ruling out mutation.

Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug.

Metformin is widely used in the treatment of type 2 diabetes (T2D), but its mechanism of action is poorly defined. Recent evidence implicates the gut microbiota as a site of metformin action. In a double-blind study, we randomized individuals with treatment-naive T2D to placebo or metformin for 4 months and showed that metformin had strong effects on the gut microbiome. These results were verified in a subset of the placebo group that switched to metformin 6 months after the start of the trial. Transfer of fecal samples (obtained before and 4 months after treatment) from metformin-treated donors to germ-free mice showed that glucose tolerance was improved in mice that received metformin-altered microbiota. By directly investigating metformin-microbiota interactions in a gut simulator, we showed that metformin affected pathways with common biological functions in species from two different phyla, and many of the metformin-regulated genes in these species encoded metalloproteins or metal transporters. Our findings provide support for the notion that altered gut microbiota mediates some of metformin's antidiabetic effects.

Obesity changes the human gut mycobiome.

The human intestine is home to a diverse range of bacterial and fungal species, forming an ecological community that contributes to normal physiology and disease susceptibility. Here, the fungal microbiota (mycobiome) in obese and non-obese subjects was characterized using Internal Transcribed Spacer (ITS)-based sequencing. The results demonstrate that obese patients could be discriminated by their specific fungal composition, which also distinguished metabolically "healthy" from "unhealthy" obesity. Clusters according to genus abundance co-segregated with body fatness, fasting triglycerides and HDL-cholesterol. A preliminary link to metabolites such as hexadecanedioic acid, caproic acid and N-acetyl-L-glutamic acid was also found. Mucor racemosus and M. fuscus were the species more represented in non-obese subjects compared to obese counterparts. Interestingly, the decreased relative abundance of the Mucor genus in obese subjects was reversible upon weight loss. Collectively, these findings suggest that manipulation of gut mycobiome communities might be a novel target in the treatment of obesity.

Circulating hepcidin in type 2 diabetes: A multivariate analysis and double blind evaluation of metformin effects.

SCOPE: Very few studies have evaluated serum hepcidin in patients with type 2 diabetes and they have reported conflicting results. In addition, the effect of antidiabetic drugs on circulating hepcidin has not been explored so far. The aims of the study were to evaluate hepcidin concentrations and hepcidin/ferritin ratio in type 2 diabetes subjects and healthy non-diabetic controls and to evaluate the effect of metformin on hepcidin concentrations. METHODS AND RESULTS: Study 1: Cross-sectional multivariate study of 239 non-diabetic individuals and 65 people with type 2 diabetes. The multivariate analysis included covariates of chronic inflammation, BMI, pharmacological treatment, menopausal status and insulin resistance. Study 2: Randomized, double-blinded, placebo-controlled 4-month trial metformin compared to placebo among 36 type 2 diabetic patients. In both groups diet was controlled by maintaining a hypocaloric intake across the trial. Hepcidin levels were significantly lower in patients with type 2 diabetes than in non-diabetic individuals either in crude or adjusted regression models (P<0.05). Hepcidin decreased in both arms of the trial (Placebo, p = 0.004; metformin, p = 0.022). CONCLUSION: Circulating hepcidin was significantly and independently lower in type 2 diabetes. Metformin treatment is not associated with reductions in hepcidin but hypocaloric diet could be involved.

Profiling of circulating microRNAs reveals common microRNAs linked to type 2 diabetes that change with insulin sensitization.

OBJECTIVE: This study sought to identify the profile of circulating microRNAs (miRNAs) in type 2 diabetes (T2D) and its response to changes in insulin sensitivity. RESEARCH DESIGN AND METHODS: The circulating miRNA profile was assessed in a pilot study of 12 men: 6 with normal glucose tolerance (NGT) and 6 T2D patients. The association of 10 circulating miRNAs with T2D was cross-sectionally validated in an extended sample of 45 NGT vs. 48 T2D subjects (65 nonobese and 28 obese men) and longitudinally in 35 T2D patients who were recruited in a randomized, double-blinded, and placebo-controlled 3-month trial of metformin treatment. Circulating miRNAs were also measured in seven healthy volunteers before and after a 6-h hyperinsulinemic-euglycemic clamp and insulin plus intralipid/heparin infusion. RESULTS: Cross-sectional studies disclosed a marked increase of miR-140-5p, miR-142-3p, and miR-222 and decreased miR-423-5p, miR-125b, miR-192, miR-195, miR-130b, miR-532-5p, and miR-126 in T2D patients. Multiple linear regression analyses revealed that miR-140-5p and miR-423-5p contributed independently to explain 49.5% (P < 0.0001) of fasting glucose variance after controlling for confounders. A discriminant function of four miRNAs (miR-140-5p, miR-423-5p, miR-195, and miR-126) was specific for T2D with an accuracy of 89.2% (P < 0.0001). Metformin (but not placebo) led to significant changes in circulating miR-192 (49.5%; P = 0.022), miR-140-5p (-15.8%; P = 0.004), and miR-222 (-47.2%; P = 0.03), in parallel to decreased fasting glucose and HbA1c. Furthermore, while insulin infusion during clamp decreased miR-222 (-62%; P = 0.002), the intralipid/heparin mixture increased circulating miR-222 (163%; P = 0.015) and miR-140-5p (67.5%; P = 0.05). CONCLUSIONS: This study depicts the close association between variations in circulating miRNAs and T2D and their potential relevance in insulin sensitivity.

Gut microbiota interactions with obesity, insulin resistance and type 2 diabetes: did gut microbiote co-evolve with insulin resistance?

PURPOSE OF REVIEW: The prevalence of obesity, insulin resistance and type 2 diabetes has steadily increased in the last decades. In addition to the genetic and environmental factors, gut microbiota may play an important role in the modulation of intermediary phenotypes leading to metabolic disease. RECENT FINDINGS: Obesity and type 2 diabetes are associated with specific changes in gut microbiota composition. The mechanisms underlying the association of specific gut microbiota and metabolic disease include increasing energy harvest from the diet, changes in host gene expression, energy expenditure and storage, and alterations in gut permeability leading to metabolic endotoxemia, inflammation and insulin resistance. In some studies, the modifications of gut microbiota induced by antibiotics, prebiotics and probiotics led to improved inflammatory activity in parallel to amelioration of insulin sensitivity and decreased adiposity. However, these effects were mainly observed in animal models. Their extrapolation to humans awaits further studies. SUMMARY: The fascinating role of gut microbiota on metabolic disease opens new avenues in the treatment of obesity, insulin resistance and type 2 diabetes. A co-evolutionary clue for microbiota and insulin resistance is suggested.

Enhanced algorithm for glucose estimation using the continuous glucose monitoring system.

BACKGROUND: The CGMS Gold continuous glucose monitor presents a problem of lack of accuracy, especially in the lower range, sometimes leading to missed or false alarms. The new algorithm aims to improve the measurement accuracy and hypoglycemia detection. MATERIAL/METHODS: Twenty-one patients with type 1 diabetes were monitored for 3 days (1 day at the hospital and 2 at home) using the CGMS Gold. For these patients, blood glucose samples were taken every 15 minutes for 2 hours after meals and every half hour otherwise during the first day. A new calibration algorithm was developed and implemented using CGMS Gold intensity readings and capillary glucose. RESULTS: After 1 day, a comparison of results from either the CGMS Gold algorithm and the proposed algorithm, compared with results from blood (2450 points), showed an increase of data in zone A with the proposed algorithm (4.4% in the Clarke error grid analysis (EGA) and 5.0% in the Consensus EGA). After comparing for 3 days, a reduction of 24.7%, p<0.05, in the overall median relative absolute difference (RAD) was also obtained. In the hypoglycemic range, a significant decrease in median RAD was observed (64.4%, p<0.05). Furthermore, the undetected hypoglycemia events in capillary samples by the proposed algorithm were reduced by 59.8% compared to the CGMS Gold algorithm. CONCLUSIONS: The performance as measured with clinical and numerical accuracy criteria illustrates the improved accuracy of the proposed algorithm in comparison with the CGMS Gold algorithm. A significant improvement in hypoglycemia detection was observed.

Real-time glucose estimation algorithm for continuous glucose monitoring using autoregressive models.

BACKGROUND: Continuous glucose monitors (CGMs) present a problem of lack of accuracy, especially in the lower range, sometimes leading to missed or false hypoglycemia. A new algorithm is presented here aimed at improving the measurement accuracy and hypoglycemia detection. Its core is the estimation of blood glucose (BG) in real time (RT) from CGM intensity readings using autoregressive (AR) models. METHODS: Eighteen patients with type 1 diabetes were monitored for three days (one at the hospital and two at home) using the CGMS Gold. For these patients, BG samples were taken every 15 min for 2 h after meals and every half hour otherwise during the first day. The relationship between the current measured by the CGMS Gold and BG was learned by an AR model, allowing its RT estimation. New capillary glucose measurements were used to correct the model BG estimations. RESULTS: A total of 563 paired points were obtained from BG and monitor readings to validate the new algorithm. 98.5% of paired points fell in zones A+B of the Clarke error grid analysis with the proposed algorithm. The overall mean and median relative absolute differences (RADs) were 9.6% and 6.7%. Measurements meeting International Organization for Standardization (ISO) criteria were 88.7%. In the hypoglycemic range, the mean and median RADs were 8.1% and 6.0%, and measurements meeting ISO criteria were 86.7%. The sensitivity and specificity with respect to hypoglycemia detection were 91.5% and 95.0%. CONCLUSIONS: The performance measured with both clinical and numerical accuracy metrics illustrates the improved accuracy of the proposed algorithm compared with values presented in the literature. A significant improvement in hypoglycemia detection was also observed.

Circulating bactericidal/permeability-increasing protein (BPI) is associated with serum lipids and endothelial function.

Bactericidal/permeability-increasing protein (BPI), a major constituent of neutrophils that possesses anti-inflammatory properties, shows a structure similar to some proteins implicated in lipid metabolism. We evaluated circulating BPI as a biomarker of endothelial function and lipid metabolism. Circulating BPI concentrations (ELISA) and serum lipids were measured in 202 Caucasian non-smoking men. In a subgroup of 91 consecutive subjects brachial vascular reactivity (high resolution external ultrasound) was assessed. Plasma BPI concentrations were positively associated with total cholesterol (TC), LDL cholesterol (LDL-C) and HDL cholesterol (HDL-C) (r= 0.203, 0.204 and 0.18; all p<0.05, respectively). In a multiple linear regression analysis, BPI levels were independent contributors to the variance of HDL-C, total cholesterol and LDL-cholesterol after adjusting for age, body mass index and glucose tolerance status. Plasma BPI concentration correlated positively with endothelium-dependent vasodilatation (r=0.277; p<0.05) and HDL-C (r=0.36; p<0.05) in subjects with normal glucose tolerance. In conclusion, circulating BPI could constitute a biomarker of lipid metabolism in subjects with normal glucose tolerance and could help to identify those subjects with preserved endothelial function.