Gut Molecules in Cardiometabolic Diseases: The Mechanisms behind the Story.

Atherosclerotic cardiovascular disease is the most common cause of morbidity and mortality worldwide. Diabetes mellitus increases cardiovascular risk. Heart failure and atrial fibrillation are associated comorbidities that share the main cardiovascular risk factors. The use of incretin-based therapies promoted the idea that activation of alternative signaling pathways is effective in reducing the risk of atherosclerosis and heart failure. Gut-derived molecules, gut hormones, and gut microbiota metabolites showed both positive and detrimental effects in cardiometabolic disorders. Although inflammation plays a key role in cardiometabolic disorders, additional intracellular signaling pathways are involved and could explain the observed effects. Revealing the involved molecular mechanisms could provide novel therapeutic strategies and a better understanding of the relationship between the gut, metabolic syndrome, and cardiovascular diseases.

Variability in Sleep Timing and Dietary Intake: A Scoping Review of the Literature.

The objective of this scoping review was to summarize previous studies which examined the effect of day-to-day variability in sleep timing and social jetlag (SJL) on dietary intake. A systematic literature search was conducted in PubMed, Embase, and Clarivate Analytics Web of Science and we identified 22 records. No difference in caloric and macronutrient intake between SJL groups was observed in studies that enrolled healthy young adults. However, studies that enrolled participants with obesity and obesity-related chronic conditions reported a higher caloric intake and a higher intake of carbohydrates, total fat, saturated fats, and cholesterol in participants with SJL than in those without. Most studies reported a lower quality of diet, a delayed mealtime, and eating jetlag in participants with SJL vs. those without SJL. No correlation of day-to-day variability in sleep timing with average caloric intake was observed, but bed-time variability was negatively associated with diet quality. Methodological issues have been identified in sources assessed including study design, power calculation, population enrolled, and tools/metrics used for sleep timing variability assessment. Future well powered longitudinal studies, with clear protocols, standardized metrics, including all age groups from general population are needed to clarify the dietary intake consequences of variability in sleep timing.

The Relationships between Gut Microbiota and Diabetes Mellitus, and Treatments for Diabetes Mellitus.

Diabetes mellitus is considered to be a global epidemic. The combination of genetic susceptibility and an unhealthy lifestyle is considered to be the main trigger of this metabolic disorder. Recently, there has been increased interest in the roles of gut microbiota as a new potential contributor to this epidemic. Research, in recent years, has contributed to an in-depth characterization of the human microbiome and its associations with various diseases, including metabolic diseases and diabetes mellitus. It is known that diet can change the composition of gut microbiota, but it is unclear how this, in turn, may influence metabolism. The main objective of this review is to evaluate the pathogenetic association between microbiota and diabetes and to explore any new therapeutic agents, including nutraceuticals that may modulate the microbiota. We also look at several mechanisms involved in this process. There is a clear, bidirectional relationship between microbiota and diabetes. Current treatments for diabetes influence microbiota in various ways, some beneficial, but others with still unclear effects. Microbiota-aimed treatments have seen no real-world significant effects on the progression of diabetes and its complications, with more studies needed in order to find a really beneficial agent.

Short-Chain Fatty Acids as Bacterial Enterocytes and Therapeutic Target in Diabetes Mellitus Type 2.

Diabetes mellitus is a disease with multiple gastrointestinal symptoms (diarrhea or constipation, abdominal pain, bloating) whose pathogenesis is multifactorial. The most important of these factors is the enteric nervous system, also known as the "second brain"; a part of the peripheral nervous system capable of functioning independently of the central nervous system. Modulation of the enteric nervous system can be done by short-chain fatty acids, which are bacterial metabolites of the intestinal microbiota. In addition, these acids provide multiple benefits in diabetes, particularly by stimulating glucagon-like peptide 1 and insulin secretion. However, it is not clear what type of nutraceuticals (probiotics, prebiotics, and alimentary supplements) can be used to increase the amount of short-chain fatty acids and achieve the beneficial effects in diabetes. Thus, even if several studies demonstrate that the gut microbiota modulates the activity of the ENS, and thus, may have a positive effect in diabetes, further studies are needed to underline this effect. This review outlines the most recent data regarding the involvement of SCFAs as a disease modifying agent in diabetes mellitus type 2. For an in-depth understanding of the modulation of gut dysbiosis with SCFAs in diabetes, we provide an overview of the interplay between gut microbiota and ENS.

The association study of high-sensitivity C-reactive protein, pentraxin 3, nitrotyrosine, and insulin dose in patients with insulin-treated type 2 diabetes mellitus.

PURPOSE: The objective of this study was to examine the association between insulin dose and high-sensitivity C-reactive protein (hsCRP), nitrotyrosine, and pentraxin 3 in patients with insulin-treated type 2 diabetes. PATIENTS AND METHODS: Eighty patients with type 2 diabetes treated with insulin for >6 months and with stable insulin doses (±10%) within 3 months before inclusion were enrolled in this study. Medical history, including use of insulin and insulin doses, concomitant diseases and medication, and anthropometric and routine biochemical parameters were collected for each patient. hsCRP, nitrotyrosine, and pentraxin 3 were measured in fasting conditions. Comparison analysis was performed according to the distribution in tertiles of insulin dose/kg of body weight, and linear regression adjusted for confounding factors was used to examine the associations between markers of inflammation, oxidative stress, and insulin dose. RESULTS: In the comparison analysis, no statistically significant difference was found between hsCRP, nitrotyrosine, and pentraxin 3 levels across tertiles of insulin dose expressed as IU/kg of body weight (p for trend >0.05 for all comparisons) except a significantly higher hsCRP level in tertile 3 compared to tertile 1 (3.9±3.6 vs 6.1±3.8 mg/dL, p=0.035). In regression analysis, after adjustment for age, gender, smoking, body mass index, glycated hemoglobin, C-peptide, metformin, antiplatelet, and statin use, only hsCRP levels were statistically significant associated with insulin dose/kg of body weight (ß=0.237, p=0.043). CONCLUSION: In this sample of patients with type 2 diabetes treated with insulin for >6 months, hsCRP was positively associated with insulin doses. No such association was found for pentraxin 3, a more specific marker of vascular inflammation, and for nitrotyrosine as a marker of oxidative stress.

HbA1c levels are associated with severity of hypoxemia and not with apnea hypopnea index in patients with type 2 diabetes: Results from a cross-sectional study.

BACKGROUND: The aim of the present study was to evaluate the effect of untreated sleep apnea syndrome (SAS) on glycemic control, evaluated by HbA1c, in patients with type 2 diabetes (T2D). METHODS: The study sample consisted of 100 consecutive adult (≥18 years) patients with T2D without a prior diagnosis of sleep apnea recruited from an outpatient diabetes clinic. All patients underwent an in-hospital cardiorespiratory study using a three-channel portable sleep diagnostic tool; 64 were found to have sleep apnea, 36 were not. Information on medical history, body weight, height, sleep apnea symptoms, Epworth Sleepiness Scale scores, and HbA1c and fasting plasma glucose levels were recorded. RESULTS: After adjusting for factors known to affect HbA1c (gender, age, diabetes duration, diabetes treatment, body mass index [BMI], and waist circumference), HbA1c was higher in patients with than without SAS (8.4 % vs 7.6 %, respectively; P = 0.04). A positive correlation was found between the presence of sleep apnea and HbA1c (r = 0.24; P = 0.02). After adjusting for confounding factors (including BMI), only mean and lowest O2 saturation during sleep were significantly associated with HbA1c (ß = -0.23 [P = 0.03] and ß = -0.24 [P = 0.007], respectively). After further adjusting for waist circumference, only lowest O2 saturation during recording remained independently associated with HbA1c (ß = -0.21; P = 0.05). CONCLUSIONS: The presence of sleep apnea is associated with poorer glycemic control in patients with T2D. In patients with sleep apnea and T2D, greater levels of oxygen desaturation are associated with poorer glycemic control.

Prevalence and predictors of non-alcoholic fatty liver disease as defined by the fatty liver index in a type 2 diabetes population.

BACKGROUND AND AIMS: We aimed to study the prevalence and the predictive factors of non-alcoholic fatty liver disease (NAFLD) defined by the fatty liver index (FLI) in type 2 diabetic patients (T2DM). METHODS: Three hundred and eighty-one T2DM outpatients who regularly attended a Consulting Clinic in Cluj were retrospectivelly included. FLI, a surrogate steatosis biomarker based on body mass index (BMI), waist circumference (WC), triglycerides (TGL) and gammaglutamyl-transferase (GGT) was used to assess NAFLD in all patients. Anthropometric and biochemical parameters were measured. Hepatic steatosis (HS) was evaluated by ultrasonography. RESULTS: NAFLD-FLI (defined as FLI>60) was correlated with HS evaluated by ultrasound (r=0.28; p<0.001). NAFLD-FLI was detected in 79% of T2DM. The prevalence of obesity in NAFLD-FLI patients was 80%. Of the patients with normal alanine aminotransferase (ALAT), 73.8 % had NAFLD. At univariate analysis, NAFLD-FLI was correlated with age (r= -0.14; p=0.007), sex (r=0.20; p<0.001), LDL cholesterol (r=0.12; p=0.032), HDL cholesterol (r = -0.13; p=0.015), ALAT (r=0.20; p<0.001) and ASAT (r=0.19; p<0.001). At multiple regression analysis, sex, ALAT and LDL-cholesterol were independent predictors of NAFLD-FLI. After logistic regression model, ALAT, LDL-cholesterol, HOMA-IR were good independent predictors of NAFLD-FLI. CONCLUSIONS: NAFLD-FLI could be useful to identify NAFLD in T2DM patients. Subjects with T2DM had a high prevalence of NADLD-FLI even with normal ALAT levels. Our findings showed that sex, ALAT, LDL cholesterol and IR were significant and independent factors associated with the presence of NAFLD in T2DM subjects.

Age, abdominal obesity, and glycated hemoglobin are associated with carotid atherosclerosis in type 2 diabetes patients with nonalcoholic fatty liver disease.

AIM: The aim of this study was to evaluate the impact of clinical parameters and indices of body composition on the relation between non-alcoholic fatty liver disease (NAFLD) and carotid intima-media thickness (cIMT), in a type 2 diabetes mellitus population (T2DM). MATERIAL AND METHODS: We retrospectively enrolled 336 T2DM outpatients who regularly attended Regina Maria Clinic in Cluj. Clinical, anthropometric and biochemical parameters were measured. Ultrasonography (US) was used to assess hepatic steatosis (HS) in all patients and cIMT in 146 subjects. Body composition was assessed by bioelectric impedance (BIA, InBody 720) in all patients. RESULTS: cIMT was correlated with age (r=0.25; p=0.004), systolic blood pressure (r=0.18; p=0.041), glycated haemoglobin A1C (HbA1C, r=0.20; p=0.04), and with coronary artery disease (r=0.20; p=0.007). HS did not correlate with cIMT (r=0.04; p=0.64). cIMT was correlated with visceral fatty area (VFA, r=0.18; p=0.014) but not with other indices of body composition. Homeostasis model assessment for insulin resistance (HOMA-IR) was not correlated with cIMT (r=0.17; p=0.086). After multivariate analysis, age, HbA1c, and VFA were good independent predictors of cIMT (r=0.45; p < 0.001). CONCLUSIONS: These results are suggestive that in T2DM patients, fatty liver is not a direct mediator of early carotid atherosclerosis. Our data indicate that visceral fat accumulation and HbA1C are determinant factors of cIMT sugesting that controlling abdominal obesity and hyperglicemia might reduce atherosclerotic disease risk in NAFLD-T2DM subjects.