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.

Diabetic retinopathy (DR), the most common microvascular complication of diabetes and leading cause of visual impairment in adults worldwide, is suggested to be linked to abnormal lipid metabolism. The present study aims to comprehensively investigate the relationship between n-6 polyunsaturated fatty acids (PUFAs) and DR. This was a propensity score matching based case-control study, including 69 pairs of DR patients and type 2 diabetic patients without DR with mean age of 56.7 ± 9.2 years. Five n-6 PUFAs were determined by UPLC-ESI-MS / MS system. Principle component regression (PCR) and multiple conditional logistic regression models were used to investigate the association of DR risk with n-6 PUFAs depending on independent training and testing sets, respectively. According to locally weighted regression model, we observed obvious negative correlation between levels of five n-6 PUFAs (linoleic acid, γ-linolenic acid, eicosadienoic acid, dihomo-γ-linolenic acid and arachidonic acid) and DR. Based on multiple PCR model, we also observed significant negative association between the five n-6 PUFAs and DR with adjusted OR (95% CI) as 0.62 (0.43,0.87). When being evaluated depending on the testing set, the association was still existed, and PCR model had excellent classification performance, in which area under the curve (AUC) was 0.88 (95%CI: 0.78, 0.99). In addition, the model also had valid calibration with a non-significant Hosmer-Lemeshow Chi-square of 9.44 (P = 0.307) in the testing set. n-6 PUFAs were inversely associated with the presence of DR, and the principle component could be potential indicator in distinguishing DR from other T2D patients.

Objective Early identification of diabetic retinopathy (DR) is key to prioritizing therapy and preventing permanent blindness. This study aims to propose a machine learning model for DR early diagnosis using metabolomics and clinical indicators. Methods From 2017 to 2018, 950 participants were enrolled from two affiliated hospitals of Wenzhou Medical University and Anhui Medical University. A total of 69 matched blocks including healthy volunteers, type 2 diabetes, and DR patients were obtained from a propensity score matching-based metabolomics study. UPLC-ESI-MS/MS system was utilized for serum metabolic fingerprint data. CART decision trees (DT) were used to identify the potential biomarkers. Finally, the nomogram model was developed using the multivariable conditional logistic regression models. The calibration curve, Hosmer–Lemeshow test, receiver operating characteristic curve, and decision curve analysis were applied to evaluate the performance of this predictive model. Results The mean age of enrolled subjects was 56.7 years with a standard deviation of 9.2, and 61.4% were males. Based on the DT model, 2-pyrrolidone completely separated healthy controls from diabetic patients, and thiamine triphosphate (ThTP) might be a principal metabolite for DR detection. The developed nomogram model (including diabetes duration, systolic blood pressure and ThTP) shows an excellent quality of classification, with AUCs (95% CI) of 0.99 (0.97–1.00) and 0.99 (0.95–1.00) in training and testing sets, respectively. Furthermore, the predictive model also has a reasonable degree of calibration. Conclusions The nomogram presents an accurate and favorable prediction for DR detection. Further research with larger study populations is needed to confirm our findings.

ObjectivesDespite the improved survival of patients with AIDS and Kaposi's sarcoma (KS), competing events are a non‐negligible issue affecting the survival of such patients. In this study, we explored the prognostic factors of KS‐specific and non‐KS‐specific mortality in patients with AIDS‐related KS (AIDS‐KS), accounting for competing risk.MethodsWe identified 17 103 patients with AIDS‐KS aged 18–65 years between 1980 and 2016 from the Surveillance, Epidemiology, and End Results (SEER) 18 registry database. Prognostic factors for KS‐specific and non‐KS‐specific mortality were determined by the Fine and Grey proportional subdistribution hazard model. We built competing risk nomograms and assessed their predictive performance based on the identified prognostic factors.ResultsIn total, 12 943 (75.68%) patients died, 1965 (15.50%) of whom died from competing events. The KS‐specific mortality rate was 14 835 per 100 000 person‐years, and the non‐KS specific mortality rate was 2719 per 100 000 person‐years. Specifically, age >44 years was associated with an 11% decrease in the subdistribution hazard of KS‐specific mortality compared with age <43 years but a 50% increase in the subdistribution hazard of non‐KS‐specific mortality. Being male was associated with a 26% increase in the subdistribution hazard of KS‐specific mortality compared with being female but a 32% decrease in the subdistribution hazard of non‐KS‐specific mortality. Notably, being in the antiretroviral therapy (ART) era consistently showed a decrease in the subdistribution hazard of both KS‐specific and non‐KS‐specific mortality than being in the pre‐ART era.ConclusionsCompeting events commonly occurred among patients with AIDS‐KS, which deserves further attention to improve the prognosis of these patients.

Background: Previous studies concerning the effect of plasma hemoglobin (HB) and other factors that may modify the risk of death in people living with HIV/AIDS (PLHIV) treated with antiretroviral therapy (ART) are limited. Results: Higher HB was independently linked to a lower death risk in PLHIV, with a decrease of 29% (13%, 43%) per standard deviation (SD) increment after adjusting for CD4, VL and other potential factors [hazard ratio (HR): 0.71, 95% confidence interval (CI): 0.57-0.87, P<0.001]. In addition, the addition of HB to the predictive model containing VL and CD4 significantly improved the C-index, by 0.69% (95% CI: 0.68%-0.71%), and net discrimination, by 0.5% (95% CI: 0.0%-1.6%, P=0.040), when predicting the death risk of PLHIV. Conclusions: A lower level of HB was an independent risk factor for HIV/AIDS-associated death in PLHIV. HB combined with VL and CD4 may be an appropriate predictive model of the death risk of PLHIV. Materials and methods: A propensity-score matching (PSM) approach was applied to select a total of 750 PLHIV (150 deceased and 600 living) from the AIDS prevention and control information system in the Wenzhou area from 2006 to 2018. Multivariable Cox proportional hazards regression models were formulated to estimate the effect of HB. The predictive performance improvement contributed by HB was evaluated using the C-index and net reclassification improvement.

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Background Optimal ω-6/ω-3 polyunsaturated fatty acids ratio (PUFAR) is reported to exert protective effects against chronic diseases. However, data on PUFAR and diabetic retinopathy (DR) remains scarce. We aimed to thoroughly quantify whether and how PUFAR was related to DR as well as its role in DR detection. Methods This two-centre case-control study was conducted from August 2017 to June 2018 in China, participants were matched using a propensity score matching algorithm. We adopted multivariable logistic regression models and restricted cubic spline analyses to estimate the independent association of PUFAR with DR, adjusting for confounders identified using a directed acyclic graph. The value of PUFAR as a biomarker for DR identification was further evaluated by receiver operating characteristic analyses and Hosmer-Lemeshow tests. Findings An apparent negative relationship between PUFAR and DR was observed. Adjusted odds of DR decreased by 79% (OR: 0·21, 95% CI: 0·10–0·40) with an interquartile range increase in PUFAR. Similar results were also obtained in tertile analysis. As compared to those in the 1st tertile of PUFAR, the adjusted odds of DR decreased by 76% (OR: 0·24, 95% CI: 0·08–0·66) and 93% (OR: 0·07, 95% CI: 0·03–0·22) for subjects in the 2nd and 3rd tertiles, respectively. Good calibration and discrimination of the PUFAR associated predictive model were detected and PUFAR = 35 would be an ideal cut-off value for DR identification. Interpretation Our results suggest that serum PUAFR is inversely associated with DR. Although PUFAR-alteration is not observed amongst different stages of DR, it can serve as an ideal biomarker in distinguishing patients with DR from those without DR. Funding This study was funded by Natural Science Foundation of Zhejiang Province, Zhejiang Basic Public Welfare Research Project, the Major Project of the Eye Hospital of Wenzhou Medical University, and the Academician's Science and Technology Innovation Program in Zhejiang province. Part of this work was also funded by the National Nature Science Foundation of China, and Research Project for College Students in Wenzhou Medical University.