Background and Objectives. Diabetic kidney disease is a leading cause of chronic kidney disease and end-stage renal disease across the world. Early identification of DKD is vitally important for the effective prevention and control of it. However, the available indicators are doubtful in the early diagnosis of DKD. This study is aimed at determining novel sensitive and specific biomarkers to distinguish DKD from their counterparts effectively based on the widely targeted metabolomics approach. Materials and Method. This case-control study involved 44 T2DM patients. Among them, 24 participants with DKD were defined as the cases and another 20 without DKD were defined as the controls. The ultraperformance liquid chromatography-electrospray ionization-tandem mass spectrometry system was applied for the assessment of the serum metabolic profiles. Comprehensive analysis of metabolomics characteristics was conducted to detect the candidate metabolic biomarkers and assess their capability and feasibility. Result. A total of 11 differential metabolites, including Hexadecanoic Acid (C16:0), Linolelaidic Acid (C18:2N6T), Linoleic Acid (C18:2N6C), Trans-4-Hydroxy-L-Proline, 6-Aminocaproic Acid, L-Dihydroorotic Acid, 6-Methylmercaptopurine, Piperidine, Azoxystrobin Acid, Lysopc 20:4, and Cuminaldehyde, were determined as the potential biomarkers for the DKD early identification, based on the multivariable generalized linear regression model and receiver operating characteristic analysis. Conclusion. Serum metabolites might act as sensitive and specific biomarkers for DKD early detection. Further longitudinal studies are needed to confirm our findings.

Accurate 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). All 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. Independent 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. The nomogram presents accurate and favourable prognostic prediction for PLHIV who underwent ART. This work was supported by Zhejiang Basic Public Welfare Research Project (LGF19H260011), Wenzhou Basic Public Welfare Research Project (Y20180201), the Initial Scientific Research Fund (KYQD170301), the Major Project of the Eye Hospital Wenzhou the Major Project of the Eye Hospital Wenzhou Medical University (YNZD201602). Part of this work was also funded by National Natural Science Foundation of China (81670777) and Science and Technology Innovation Activity Plan and New Talents Plan for College Students in Zhejiang Province (2019R413073). The funders had no roles in study design, data collection, data analysis, interpretation and writing of the report.

Introduction Despite 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 methods In 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. Results We 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. Conclusions This 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.

Myopia is a major public health issue and occurs at young ages. Apart from its high prevalence, myopia results in high costs and irreversible blinding diseases. Accurate prediction of the risk of myopia onset is crucial for its precise prevention. We aimed to develop and validate an effective nomogram for predicting myopia onset in schoolchildren. School-based prospective cohort study. A total of 1073 schoolchildren were enrolled from November 2014 to May 2019 in China, and were divided into the training and validation cohorts. Myopia was defined as a spherical equivalent refraction (SER) ≤-0.5 diopters. Predictors of myopia were determined through the least absolute shrinkage and selection operator regression and multivariable Cox proportional hazard model based on the training cohort. The predictive performance of the nomogram was validated internally through time-dependent receiver operating characteristic (ROC) curves, calibration plot, decision curve analysis, and Kaplan-Meier curves. Independent predictors at baseline including gender, SER, axial length, corneal refractive power, and positive relative accommodation were included in the nomogram prediction model. This nomogram demonstrated excellent calibration, clinical net benefit, and discrimination, with all the area under the ROC curves (AUCs) between 0.74 and 0.86 in the training and validation cohorts. The Kaplan-Meier curves showed that 3 distinct risk groups stratified through X-tile analysis were well discriminated and robust among subgroups. The Harrell's C-index and net reclassification improvement demonstrated that the nomogram substantially improved compared with previous models. An online myopia risk calculator was generated for better individual prediction. The nomogram provides accurate and individual prediction of myopia onset in schoolchildren. External validation is needed to verify the generalizability of this nomogram.

Chronic arsenic exposure via drinking water has become a worldwide public health concern. In humans, inorganic arsenic (iAs) is metabolized to monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA) mainly mediated by arsenic (+3 oxidation state) methyltransferase (As3MT). We reported recently that N-6 adenine-specific DNA methyltransferase 1 (N6AMT1) was involved in arsenic metabolism, and examined its interactive effect with As3MT on arsenic metabolism in vitro. To further evaluate the interactive effect of N6AMT1 and As3MT on arsenic biomethylation in humans, we conducted a human population-based study including 289 subjects living in rural villages in Inner Mongolia, China, and assessed their urinary arsenic metabolites profiles in relation to genetic polymorphisms and haplotypes of N6AMT1 and As3MT. Five N6AMT1 single nucleotide polymorphisms (SNPs; rs1003671, rs7282257, rs2065266, rs2738966, rs2248501) and the N6AMT1 haplotype 2_GGCCAT were significantly associated with the percentage of iAs (% iAs) in urine (e.g., for rs7282257, mean was 9.62% for TT, 6.73% for AA). Rs1003671 was also in a significant relationship with urinary MMA and DMA (the mean of %MMA was 24.95% for GA, 31.69% for GG; the mean of % DMA was 69.21% for GA, 59.82% for GG). The combined effect of N6AMT1 haplotype 2_GGCCAT and As3MT haplotype 2_GCAC showed consistence with the additive significance of each haplotype on % iAs: the mean was 5.47% and 9.36% for carriers with both and null haplotypes, respectively. Overall, we showed that N6AMT1 genetic polymorphisms were associated with arsenic biomethylation in the Chinese population, and its interaction with As3MT was observed in specific haplotype combinations.

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.