Association of serum uric acid with anemia in U.S. adults: a cross-sectional study using secondary data.

BACKGROUND: High serum uric acid (SUA) is a risk factor of cardiovascular disease (CVD). Abnormal SUA have been correlated with a significant increase in mortality. Anemia is an independent predictor of mortality and CVD. To date, no study has investigated the relationship between SUA and anemia. Here, we explored the correlation between SUA and anemia in the American population. METHODS: The cross-sectional study involved 9205 US adults from NHANES (2011-2014). The relationship between SUA and anemia was explored using multivariate linear regression models. Two-piecewise linear regression model, generalized additive models (GAM) and smooth curve fitting were performed to explore the non-linear relationships between SUA and anemia. RESULTS: We found a U-shaped non-linear relationship between SUA and anemia. The inflection point of the SUA concentration curve was 6.2 mg/dL. The ORs (95% CIs) for anemia on the left and right of the inflection point were 0.86 (0.78-0.95) and 1.33 (1.16-1.52), respectively. The 95% CI of inflection point was 5.9-6.5 mg/dL. The findings showed that both genders presented a U-shaped correlation. Safe ranges of SUA in men and women were 6-6.5 and 4.3-4.6 mg/dL, respectively. CONCLUSIONS: Both high and low SUA levels were correlated with increased risk of anemia, and a U-shaped relationship was observed between SUA and anemia.

In silico identification of gene amplification targets based on analysis of production and growth coupling.

Genome-scale metabolic models (GEMs) can be utilized to better understand the genotype-phenotype relationship in microbial metabolism. Manipulation strategies based on analysis of metabolic flux distributions using constraint-based methods have been validated to be effective for designing strains. Herein, we first investigated the coupled relationship of growth and production, and subsequently proposed an algorithm, called analysis of production and growth coupling (APGC), to identify amplification targets for improving production of the desired metabolite. The logical transformation of the genome-scale metabolic models (LTM) could enable a gene-level prediction, that is, direct gene targets would be determined through APGC. This algorithm was successfully employed to simulate heterogeneous biosynthesis of the antioxidant lycopene in Escherichia coli, and target genes for the improvement of lycopene production were identified. These identified gene targets were unambiguous and were closely related to the supply of essential precursors and cofactors for lycopene production, and most of these have been validated as effective in enhancing the yield of lycopene.

IdealKnock: A framework for efficiently identifying knockout strategies leading to targeted overproduction.

In recent years, computer aided redesigning methods based on genome-scale metabolic network models (GEMs) have played important roles in metabolic engineering studies; however, most of these methods are hindered by intractable computing times. In particular, methods that predict knockout strategies leading to overproduction of desired biochemical are generally unable to do high level prediction because the computational time will increase exponentially. In this study, we propose a new framework named IdealKnock, which is able to efficiently evaluate potentials of the production for different biochemical in a system by merely knocking out pathways. In addition, it is also capable of searching knockout strategies when combined with the OptKnock or OptGene framework. Furthermore, unlike other methods, IdealKnock suggests a series of mutants with targeted overproduction, which enables researchers to select the one of greatest interest for experimental validation. By testing the overproduction of a large number of native metabolites, IdealKnock showed its advantage in successfully breaking through the limitation of maximum knockout number in reasonable time and suggesting knockout strategies with better performance than other methods. In addition, gene-reaction relationship is well considered in the proposed framework.

Effect of pentoxifylline in proteinuric chronic kidney disease: a systematic review and meta-analysis.

AIM: The aim of this study was to assess the effect of pentoxifylline on proteinuria and renal function in chronic kidney disease (CKD) treatment. METHODS: We systematically searched PubMed, EMBASE, the Cochrane Library and ClinicalTrials.gov for randomized and non-randomized controlled trials comparing pentoxifylline to placebo, no treatment or renin-angiotensin system blockade in proteinuric CKD patients. The outcomes concerning proteinuria, renal function, blood pressure and adverse events were extracted. RESULTS: Twelve trials with 613 participants were identified. Pentoxifylline significantly decreased proteinuria [weighted mean difference (WMD) -0.60 g/day (95 % CI -0.84 to -0.36); p < 0.001] compared to placebo or no-treatment groups, but the decrease was not significant [WMD: 0.10 g/day (-0.34 to 0.54); p = 0.66] compared to captopril treatment. The decrease of glomerular filtration rate was significantly less [WMD: 3.67 ml/min (2.71-4.62); p < 0.001] in the pentoxifylline group than in the controls. There was no significant difference in serum creatinine [WMD: -0.03 mg/dl (-0.10 to 0.03); p = 0.28], diastolic blood pressure [WMD: 0.94 mmHg (-0.74 to 2.61); p = 0.27] and adverse events [RR: 0.89 (0.60 to 1.32); p = 0.56]. CONCLUSIONS: Pentoxifylline may decrease proteinuria and protect renal function in patients with CKD. Further studies are needed to confirm this result.

[A standardization study of Meta-analyses on nephropathy published in Chinese journals].

OBJECTIVE: To evaluate the standardization of Meta-analyses on nephropathy published in Chinese journals. METHODS: By searching in WANFANG, VIP, CNKI databases and Chinese Biomedical Literature Database (CBM) as well as related Chinese journals, eligible Meta-analyses were enrolled and analyzed according to the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement and the MOOSE (Meta-analysis of Observational Studies in Epidemiology) Checklist. RESULTS: A total of 217 Meta-analyses were enrolled with 166 on randomized controlled trials (RCT) and 51 on observational studies. Based on the PRSIMA Statement, of the 166 Meta-analyses on RCT, 51.8% (86 papers) were found with the complete research hypothesis, 13.9% (23) with the literature screening flow chart, 15.7% (26) with the subgroup analysis, 53.0% (88) with the publication bias analysis and 28.3% (47) with the sensitivity analysis. According to the MOOSE Checklist, of the 51 Meta-analyses on observational studies, only 9.8% (5) had done the statistical stability calculation, 54.9% (28) with the outlook of application, 45.1% (23) with the limitation of the study, 2.0% (1) with the quantitative analysis on potential bias and 17.6% (9) with the suggestion for future studies. CONCLUSIONS: Unclear hypothesis, limited methodological description, lack of in-depth analysis on heterogeneity and bias are the common defects in Meta-analyses published in Chinese journals on nephrology.