Objective The present study aimed to verify the effectiveness of self-monitoring of blood glucose (SMBG) in patients with non-insulin-treated type 2 diabetes (T2D). Methods A comprehensive literature search was conducted in PubMed, Cochrane Library, Web of Science, ScienceDirect and ClinicalTrials.gov from their respective inception dates to 26 October 2015. Eligible randomised controlled trials (RCTs) were included according to prespecified criteria. The quality of the included RCTs was evaluated according to the Cochrane risk of bias tool, and the evidence quality of meta-analyses was assessed by the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) criteria. A meta-analysis of primary and secondary outcome measures was performed. Sensitivity and subgroup analyses were carried out to evaluate the robustness and heterogeneity of the findings. Begg's and Egger's tests were used to quantify publication biases. Results A total of 15 RCTs, comprising 3383 patients with non-insulin-treated T2D, met the inclusion criteria. The SMBG intervention improved glycated haemoglobin (HbA1c) (mean difference −0.33; 95% CI −0.45 to −0.22; p=3.0730e−8; n=18), body mass index (BMI; −0.65; −1.18 to −0.12; p=0.0164; n=9) and total cholesterol (TC; −0.12; −0.20 to −0.04; p=0.0034; n=8) more effectively than the control in overall effect. The sensitivity analysis revealed little difference in overall effect, indicating the robustness of the results. SMBG moderated HbA1c levels better than the control in all subgroup analyses. Most of the RCTs had high risk of bias in blinding, while the overall quality of evidence for HbA1c was moderate according to the GRADE criteria. Publication bias was moderate for BMI. Conclusions SMBG improved HbA1c levels in the short term (≤6-month follow-up) and long term (≥12-month follow-up) in patients with T2D who were not using insulin. Trial registration number CRD42015019099.

Aims/hypothesis: MicroRNAs are being sought as biomarkers for the early identification of type 2 diabetes. This study aimed to synthesise the evidence from microRNA–type 2 diabetes association studies and microRNA-regulated type 2 diabetes pathway delineation studies that met stringent quality criteria to identify and validate microRNAs of both statistical and biological significance as type 2 diabetes biomarkers. Methods: Eligible controlled studies on microRNA expression profiling of type 2 diabetes were retrieved from PubMed, ScienceDirect and Web of Science. MicroRNA-regulated type 2 diabetes pathway delineation studies were conducted by integrating and cross-verifying the data from miRTarBase, TransmiR, miRecords, TargetScanHuman, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Retraction Watch database. Before meta-analysis, quality assessment was performed according to the corresponding reporting guidelines for evidence-based medicine. To select the most statistically significant microRNAs, we conducted extensive meta-analyses according to the latest methodology. Subgroup and sensitivity analyses were carried out to further examine the microRNA candidates for their tissue specificity and blood fraction specificity and the robustness of the evidence. Signalling pathway impact analysis of dysregulated microRNAs identified from meta-analyses was performed to select biologically significant microRNAs that were enriched in our newly built microRNA-regulated pathways. Results: Of the 404 differentially expressed microRNAs identified in the 156 controlled profiling studies with a combined sample size of >15,000, only 60 were both consistently and significantly dysregulated in human type 2 diabetes. No microRNAs were both consistently and significantly dysregulated in multiple tissues according to subgroup analyses. In total, 58 microRNAs were found to be robust in sensitivity analyses. A total of 1966 pathway delineation studies were identified, including 3290 microRNA–target interactions, which were further combined with KEGG pathways, producing 225 microRNA-regulated pathways. Impact analysis found that 16 dysregulated microRNAs identified from extensive meta-analyses were statistically significantly enriched in the augmented KEGG type 2 diabetes pathway. Conclusions/interpretation: Sixteen microRNAs met the criteria for biomarker selection. In terms of both significance and relevance, the order of priority for verification of these microRNAs is as follows: miR-29a-3p, miR-221-3p, miR-126-3p, miR-26a-5p, miR-503-5p, miR-100-5p, miR-101-3p, mIR-103a-3p, miR-122-5p, miR-199a-3p, miR-30b-5p, miR-130a-3p, miR-143-3p, miR-145-5p, miR-19a-3p and miR-311-3p. Registration: PROSPERO registration number CRD42017081659. Graphical

Background: Blood circulating microRNAs that are specific for Alzheimer’s disease (AD) can be identified from differentially expressed microRNAs (DEmiRNAs). However, non-reproducible and inconsistent reports of DEmiRNAs hinder biomarker development. The most reliable DEmiRNAs can be identified by meta-analysis. To enrich the pool of DEmiRNAs for potential AD biomarkers, we used a machine learning method called adaptive boosting for miRNA disease association (ABMDA) to identify eligible candidates that share similar characteristics with the DEmiRNAs identified from meta-analysis. This study aimed to identify blood circulating DEmiRNAs as potential AD biomarkers by augmenting meta-analysis with the ABMDA ensemble learning method. Methods: Studies on DEmiRNAs and their dysregulation states were corroborated with one another by meta-analysis based on a random-effects model. DEmiRNAs identified by meta-analysis were collected as positive examples of miRNA–AD pairs for ABMDA ensemble learning. ABMDA identified similar DEmiRNAs according to a set of predefined criteria. The biological significance of all resulting DEmiRNAs was determined by their target genes according to pathway enrichment analyses. The target genes common to both meta-analysis- and ABMDA-identified DEmiRNAs were collected to construct a network to investigate their biological functions. Results: A systematic database search found 7841 studies for an extensive meta-analysis, covering 54 independent comparisons of 47 differential miRNA expression studies, and identified 18 reliable DEmiRNAs. ABMDA ensemble learning was conducted based on the meta-analysis results and the Human MicroRNA Disease Database, which identified 10 additional AD-related DEmiRNAs. These 28 DEmiRNAs and their dysregulated pathways were related to neuroinflammation. The dysregulated pathway related to neuronal cell cycle re-entry (CCR) was the only statistically significant pathway of the ABMDA-identified DEmiRNAs. In the biological network constructed from 1865 common target genes of the identified DEmiRNAs, the multiple core ubiquitin-proteasome system, that is involved in neuroinflammation and CCR, was highly connected. Conclusion: This study identified 28 DEmiRNAs as potential AD biomarkers in blood, by meta-analysis and ABMDA ensemble learning in tandem. The DEmiRNAs identified by meta-analysis and ABMDA were significantly related to neuroinflammation, and the ABMDA-identified DEmiRNAs were related to neuronal CCR.

Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide-dependent deacetylase and its dysregulation can lead to ageing, diabetes and cancer. From 346 experimentally confirmed SIRT1 inhibitors, an inhibitor structure pattern was generated by inductive logic programming (ILP) with DMax Chemistry Assistant software. The pattern contained amide, amine and hetero-aromatic five-membered rings, each of which had a hetero-atom and an unsubstituted atom at a distance of 2. According to this pattern, a ligand-based virtual screening of 1 444 880 active compounds from Chinese herbs identified 12 compounds as inhibitors of SIRT1. Three compounds (ZINC08790006, ZINC08792229 and ZINC08792355) had high affinity (−7.3, −7.8 and −8.6 kcal/mol, respectively) for SIRT1 as estimated by molecular docking software AutoDock Vina. This study demonstrated a use of ILP and background knowledge in machine learning to facilitate virtual screening.

Background: Tongxinluo (TXL), consisting of 12 Chinese Materia Medica items catalogued in the Chinese Pharmacopoeia, is commercially available in China, South Korea, and Russia. Hundreds of randomized clinical trials (RCTs) on TXL in treating cardiovascular diseases were conducted and published in China. This study provides a comprehensive Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)–compliant systematic review with sensitivity and subgroup analyses to evaluate the evidence about whether TXL is more effective than isosorbide dinitrate (ISDN) in treating ischemic heart disease, particularly angina pectoris. Methods: RCTs published between 1996 and 2010 on TXL versus ISDN in treating angina pectoris for at least 4 weeks were retrieved from eight bibliographical databases (e.g., MEDLINE,^® PubMed, Chinese National Knowledge Infrastructure, Cochrane Library, and WanFang Data). The quality of RCTs was assessed with the Jadad scale. Meta-analysis was performed to estimate the overall effects based on symptomatic and electrocardiographic (ECG) improvements. Subgroup analysis, sensitivity analysis, and meta-regression were conducted on the study characteristics of RCTs. Results: Twenty (20) RCTs with a total of 1936 participants were included after eligibility assessment. The Jadad score of all included studies was 2. The means of summary odds ratios (ORs) for comparing TXL and nitrates were 3.30 (95% confidence interval [CI] 2.37–4.58) by symptoms (n=20) and 2.38 (95% CI 1.846–3.09) by ECG (n=18). There was a significant correlation of ORs between symptoms and ECG (ρ=0.77 and p=0.00026). Subgroup analysis, sensitivity analysis, and meta-regression found no significant difference in overall effects among all study characteristics except the years of publication (p=0.0409). Conclusions: The meta-analysis of 20 eligible RCTs demonstrates moderate evidence that TXL is more effective than ISDN for treating angina pectoris. This result warrants further RCTs of multicenters/countries, larger sample sizes, and higher quality.