IntroductionDespite advances in diabetic retinopathy (DR) medications, early identification is vitally important for DR administration and remains a major challenge. This study aims to develop a novel system of multidimensional network biomarkers (MDNBs) based on a widely targeted metabolomics approach to detect DR among patients with type 2 diabetes mellitus (T2DM) efficiently.Research design and methodsIn this propensity score matching-based case-control study, we used ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry system for serum metabolites assessment of 69 pairs of patients with T2DM with DR (cases) and without DR (controls). Comprehensive analysis, including principal component analysis, orthogonal partial least squares discriminant analysis, generalized linear regression models and a 1000-times permutation test on metabolomics characteristics were conducted to detect candidate MDNBs depending on the discovery set. Receiver operating characteristic analysis was applied for the validation of capability and feasibility of MDNBs based on a separate validation set.ResultsWe detected 613 features (318 in positive and 295 in negative ESI modes) in which 63 metabolites were highly relevant to the presence of DR. A panel of MDNBs containing linoleic acid, nicotinuric acid, ornithine and phenylacetylglutamine was determined based on the discovery set. Depending on the separate validation set, the area under the curve (95% CI), sensitivity and specificity of this MDNBs system were 0.92 (0.84 to 1.0), 96% and 78%, respectively.ConclusionsThis study demonstrates that metabolomics-based MDNBs are associated with the presence of DR and capable of distinguishing DR from T2DM efficiently. Our data also provide new insights into the mechanisms of DR and the potential value for new treatment targets development. Additional studies are needed to confirm our findings.

BackgroundAccurate forecast of the death risk is crucial to the administration of people living with HIV/AIDS (PLHIV). We aimed to establish and validate an effective prognosis nomogram in PLHIV receiving antiretroviral therapy (ART).MethodsAll the data were obtained from 2006 to 2018 in the Wenzhou area from China AIDS prevention and control information system. Factors included in the nomogram were determined by univariate and multiple Cox proportional hazard analysis based on the training set. The receiver operating characteristic (ROC) and calibration curves were used to assess its predictive accuracy and discriminative ability. Its clinical utility was also evaluated using decision curve analysis (DCA), X-tile analysis and Kaplan-Meier curve, respectively in an independent validation set.FindingsIndependent prognostic factors including haemoglobin, viral load and CD4+ T-cell count were determined and contained in the nomogram. Good agreement between the prediction by nomogram and actual observation could be detected in the calibration curve for mortality, especially in the first year. In the training cohort, AUC (95% CI) and C-index (95% CI) were 0.93 (0.90, 0.96) and 0.90 (0.85, 0.96), respectively. In the validation set, the nomogram still revealed excellent discriminations [AUC (95% CI): 0.95 (0.91, 1.00)] and good calibration [C-index (95% CI): 0.92 (0.82–1.00)]. Moreover, DCA also demonstrated that the nomogram was clinical beneficial. Additionally, participants could be classified into three distinct (low, middle and high) risk groups by the nomogram.InterpretationThe nomogram presents accurate and favourable prognostic prediction for PLHIV who underwent ART.FundingThis work was supported by (LGF19H260011), (Y20180201), the (KYQD170301), the Major Project of the Eye Hospital Wenzhou the Major Project of the (YNZD201602). Part of this work was also funded by (81670777) and and (2019R413073). The funders had no roles in study design, data collection, data analysis, interpretation and writing of the report.

Tobacco smoking was one of the risk factors for upper aerodigestive tract cancer, but exclusive quantification of the impact of cigarette smoking on laryngeal cancer had not been investigated. A meta-analysis of researches that had reported quantitative estimates of cigarette smoking and risk of laryngeal cancer by March 2016 was performed. Pooled estimates of relative risks and their 95% confidence intervals were obtained and summarized. Sensitivity analysis and subgroup analysis were implemented to find out sources of research heterogeneity and the effect of potential confounders. Publication bias was investigated and corrected if found to be present through Egger's and Begg's test, and trim and fill algorithm. Thirty researches based on a total of 14,292 cases from three cohort and fifteen case-control studies were included and pooled estimate for the correlation between cigarette smoking and the risk of laryngeal cancer was 7.01 (95% confidence interval 5.56-8.85), with moderate heterogeneity across the researches (I = 56.7%, p = 0.002). The RRs were 5.04 (95% CI 3.09-8.22) for cohort studies (p = 0.121), 7.59 (95% CI 5.86-9.82) for case-control studies (p = 0.005). The risk kept elevated within the first fifteen years of quitting smoking(RR 3.62, 95% CI 1.88-7.00) but dropped in the 16 years and more after smoking cessation(RR 1.88, 95% CI 1.16-3.05). Individuals who smoked with 40 or more pack-years had nine times the risk of laryngeal cancer(RR 9.14; 95% CI 6.24-13.39). Subjects who smoked 30 or more cigarettes a day had sevenfolds the risk of laryngeal cancer (RR 7.02; 95% CI 4.47-11.02) and who smoked 40 or more years had five times the risk versus never smokers (RR 5.76; 95% CI 3.69-8.99). Evidence of publication bias was not detected for the correlation between current cigarette smoking and risk of laryngeal cancer (p = 0.225 with Begg's test, p = 0.317 with Egger's test). The results demonstrated strong correlation referring to dose-response and time-response between cigarette smoking and risk of laryngeal cancer for both men and women. The probability of developing laryngeal cancer was decreased by quitting smoking, particularly among former cigarette smokers who had stopped smoking for 15 or more years. The subgroup analysis demonstrated that study type influenced the RRs estimates of the studies.