Uncurtaining the pivotal role of ABC transporters in diabetes mellitus

Potential of FGF21 in type 2 diabetes mellitus treatment based on untargeted metabolomics

Fibroblast growth factor 21 (FGF21) has promise for treating diabetes and its associated comorbidities. It has been found to reduce blood glucose in mice and humans; however, its underlying mechanism is not known. Here, the metabolic function of FGF21 in diabetes was investigated. Diabetic db/db mice received intraperitoneal injections of FGF21 for 28 days, the serum of each mouse was collected, and their metabolites were analyzed by untargeted metabolomics using UHPLC-MS/MS. It was found that FGF21 reduced blood glucose and oral glucose tolerance without causing hypoglycemia. Moreover, administration of FGF21 reduced the levels of TG and LDL levels while increasing those of HDL and adiponectin. Importantly, the levels of 45 metabolites, including amino acids and lipids, were significantly altered, suggesting their potential as biomarkers. We speculated that FGF21 may treat T2DM through the regulation of fatty acid biosynthesis, the TCA cycle, and vitamin digestion and absorption. These findings provide insight into the mechanism of FGF21 in diabetes and suggest its potential for treating diabetes.

Long-term quality-of-care score for predicting the occurrence of acute myocardial infarction in patients with type 2 diabetes mellitus

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of death globally, and diabetes mellitus (DM) is a well-established risk factor. Among the risk factors for CVD, DM is a major modifiable factor. In the fatal CVD outcomes, acute myocardial infarction (AMI) is the most common cause of death.

AIM

To develop a long-term quality-of-care score for predicting the occurrence of AMI among patients with type 2 DM on the basis of the hypothesis that good quality of care can reduce the risk of AMI in patients with DM.

METHODS

Using Taiwan’s Longitudinal Cohort of Diabetes Patient Database and the medical charts of a medical center, we identified incident patients diagnosed with type 2 DM from 1999 to 2003 and followed them until 2011. We constructed a summary quality-of-care score (with values ranging from 0 to 8) with process indicators (frequencies of HbA_1c and lipid profile testing and urine, foot and retinal examinations), intermediate outcome indicators (low-density lipoprotein, blood pressure and HbA_1c), and co-morbidity of hypertension. The associations between the score and the incidence of AMI were evaluated using Cox regression models.

RESULTS

A total of 7351 patients who had sufficient information to calculate the score were enrolled. In comparison with participants who had scores ≤ 1, those with scores between 2 and 4 had a lower risk of developing AMI [adjusted hazard ratio (AHR) = 0.71; 95% confidence interval (95%CI): 0.55-0.90], and those with scores ≥ 5 had an even lower risk (AHR = 0.37; 95%CI: 0.21-0.66).

CONCLUSION

Good quality of care can reduce the risk of AMI in patients with type 2 DM. The quality-of-care score developed in this study had a significant association with the risk of AMI and thus can be applied to guiding the care for these patients.

Integration of metabolomics and network pharmacology for enhancing mechanism understanding and medication combination recommendation for diabetes mellitus and diabetic nephropathy

With the increasing prevalence of diabetes mellitus (DM) and diabetic nephropathy (DN), effective treatment is particularly important for the recovery of patients. However, the currently approved drugs are usually tailored to clinical symptoms and no mechanism-targeted drugs are available. In this study, the combination of metabolomics and network pharmacology was applied to provide reasonable medication combination regimens to meet the different clinical needs for the targeted treatment of DM and DN. An NMR-based metabolomic strategy was applied to identify the potential urinary biomarkers of DM or/and DN, while network pharmacology was used to identify the therapy targets of DM and DN by intersecting the targets of diseases and currently approved drugs. According to the enriched signaling pathways using the potential biomarkers and the therapy targets, the specific medication combinations were recommended for the specific clinical demands in terms of hypoglycemic, hypertensive, and/or lipid-lowering. For DM, 17 potential urinary biomarkers and 12 disease-related signaling pathways were identified, and 34 combined medication regimens related to hypoglycemia, hypoglycemia, and hypertension, and hypoglycemia, hypertension, and lipid-lowering were administered. For DN, 22 potential urinary biomarkers and 12 disease-related signaling pathways were identified, and 21 combined medication regimens related to hypoglycemia, hypoglycemia, and hypertension were proposed. Molecular docking was used to verify the binding ability, docking sites, and structure of the drug molecules to target proteins. Moreover, an integrated biological information network of the drug-target-metabolite-signaling pathways was constructed to provide insights into the underlined mechanism of DM and DN as well as clinical combination therapy.

Nontargeted metabolomics to characterize the effects of isotretinoin on skin metabolism in rabbit with acne

Background: Acne vulgaris is a chronic inflammatory disease of the pilosebaceous unit. This study aimed to explore the pathogenesis of acne and the therapeutic mechanism of isotretinoin from the metabolic perspective in coal tar-induced acne in rabbits.

Methods: Ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC-qTOF-MS) based metabolomics was used to identify skin metabolites in groups C (blank control), M (model group) and T (isotretinoin group). Multivariate statistical analysis was used to process the metabolomics data.

Results: 98 differential metabolites in group C and group M were identified. The highest proportion of differential metabolites were organic acids and derivatives, lipid metabolites, organic heterocyclic compounds, and nucleoside metabolites. The most significant metabolic pathways included protein digestion and absorption, central carbon metabolism in cancer, ABC transporters, aminoacyl-tRNA biosynthesis, biosynthesis of amino acids, and sphingolipid signaling pathway. Isotretinoin treatment normalized eight of these metabolites.

Conclusions: Our study will help to further elucidate the pathogenesis of acne, the mechanism of isotretinoin at the metabolite level, and identify new therapeutic targets for treating acne.

Gut Microbiota Dysbiosis and Altered Bile Acid Catabolism Lead to Metabolic Disorder in Psoriasis Mice

Patients with psoriasis tend to have significant comorbidities, such as hyperlipemia, diabetes mellitus, and obesity, which belong to metabolic disorders. The specific mechanism through which psoriasis increases the metabolic disorder risk is uncertain. In this study, we demonstrated that the dysbiotic gut microbiota of 6-month-old psoriasis-like model mice (K14-VEGF-A-transgenic) exacerbated psoriasis disease and induced metabolic disorder when transferred into 2-month-old mice. By 16S rRNA gene sequencing, we confirmed that the Parabacteroides distasonis decreased with age in K14-VEGF mice, and P. distasonis also decreased in the transferred mice. Metabolomic screening identified an altered bile acid profile, including a decrease in chenodeoxycholic acid (CDCA) in the feces of transferred mice. Additionally, CDCA supplements prevented metabolic disorders in K14-VEGF-A-transgenic mice. Consequently, we found that aberrant bile acid metabolism may contribute to metabolic disorder in K14-VEGF-A-transgenic mice, indicating the possibility to prevent and treat the metabolic disorder in psoriasis mice by targeting gut microbial metabolites.

Acute coronary syndromes in diabetic patients, outcome, revascularization, and antithrombotic therapy

Diabetes exacerbates the progression of atherosclerosis and is associated with increased risk of developing acute coronary syndrome (ACS). Approximatively 25-30% of patients admitted for ACS have diabetes. ACS occurs earlier in diabetics and is associated with increased mortality and a higher risk of recurrent ischemic events. An increased proinflammatory and prothrombotic state is involved in the poorer outcomes of diabetic patients. In the past decade advancement in both percutaneous coronary intervention (PCI) and coronary artery by-pass graft (CABG) techniques and more potent antiplatelet drugs like prasugrel and ticagrelor improved outcomes of diabetic patients with ACS, but this population still experiences worse outcomes compared to non-diabetic patients. While in ST elevation myocardial infarction urgent PCI is the method of choice for revascularization, in patients with non-ST elevation ACS an early invasive approach is suggested by the guidelines, but in the setting of multivessel (MV) or complex coronary artery disease (CAD) the revascularization strategy is less clear. This review describes the accumulating evidence regarding factors involved in promoting increased incidence and poor prognosis of ACS in patients with diabetes, the evolution over time of prognosis and outcomes, revascularization strategies and antithrombotic therapy studied until now.

Unraveling Gut Microbiota Signatures Associated with PPARD and PARGC1A Genetic Polymorphisms in a Healthy Population

Recent studies have revealed the importance of the gut microbiota in the regulation of metabolic phenotypes of highly prevalent metabolic diseases such as obesity, type II diabetes mellitus (T2DM) and cardiovascular disease. Peroxisome proliferator-activated receptors (PPARs) are a family of ligand-activated nuclear receptors that interact with PPAR-γ co-activator-1α (PPARGC1A) to regulate lipid and glucose metabolism. Genetic polymorphisms in PPARD (rs 2267668; A/G) and PPARGC1A (rs 8192678; G/A) are linked to T2DM. We studied the association between the single-nucleotide polymorphisms (SNPs) rs 2267668 and rs 8192678 and microbiota signatures and their relation to predicted metagenome functions, with the aim of determining possible microbial markers in a healthy population. Body composition, physical exercise and diet were characterized as potential confounders. Microbiota analysis of subjects with PPARGC1A (rs 8192678) and PPARD (rs 2267668) SNPs revealed certain taxa associated with the development of insulin resistance and T2DM. Kyoto encyclopedia of gene and genomes analysis of metabolic pathways predicted from metagenomes highlighted an overrepresentation of ABC sugar transporters for the PPARGC1A (rs 8192678) SNP. Our findings suggest an association between sugar metabolism and the PPARGC1A rs 8192678 (G/A) genotype and support the notion of specific microbiota signatures as factors related to the onset of T2DM.