Iodine monitoring models contribute to avoid adverse birth outcomes related more than adequate iodine intake.

BACKGROUND: Iodine plays an important role in pregnancy. How to maintain adequate iodine intake amongst pregnant women in each trimester of pregnancy to prevent adverse birth outcomes in central China is a challenge for clinical practice. METHODS: 870 pregnant women and their infants were enrolled in the study. Urinary iodine concentration (UIC) was measured using an inductively coupled plasma mass spectrometry (ICP-MS). Maternal and newborn information were obtained during follow-up. Multinomial logistic regression models were established. RESULTS: Median UIC of pregnant women was 172 ± 135 µg/L which is currently considered to be sufficient. Multivitamin supplements containing iodine, iodized salt intake and frequent milk intake were significantly associated with higher UIC. Multivariate logistic regression analysis showed that multivitamin supplements containing iodine and milk consumption were risk factors for more than adequate iodine (UIC ≥ 250 µg/L). Iodine-rich diet was significantly related to heavier birthweight, larger head circumference and longer femur length of the newborns while more than adequate iodine intake (UIC ≥ 250 µg/L) was a risk factor for macrosomia. Logistic regression models based on potential risk factors involving iodine containing supplements and iodine-rich diet were established to predict and screen pregnant women with high risk of more than adequate iodine intake among local pregnant women in different trimesters and guide them to supplement iodine reasonably to prevent the risk. CONCLUSIONS: Multivitamin supplements containing iodine and milk consumption were risk factors for maternal UIC ≥ 250 µg/L which was a risk factor for macrosomia. Iodine monitoring models were established to provide guidance for pregnant women to reduce the risk of more than adequate iodine intake, thereby contributing to reduce the risk of having a macrosomia.

Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major public health problem and cause of mortality worldwide. However, COPD in the early stage is usually not recognized and diagnosed. It is necessary to establish a risk model to predict COPD development. METHODS: A total of 441 COPD patients and 192 control subjects were recruited, and 101 single-nucleotide polymorphisms (SNPs) were determined using the MassArray assay. With 5 clinical features as well as SNPs, 6 predictive models were established and evaluated in the training set and test set by the confusion matrix AU-ROC, AU-PRC, sensitivity (recall), specificity, accuracy, F1 score, MCC, PPV (precision) and NPV. The selected features were ranked. RESULTS: Nine SNPs were significantly associated with COPD. Among them, 6 SNPs (rs1007052, OR = 1.671, P = 0.010; rs2910164, OR = 1.416, P < 0.037; rs473892, OR = 1.473, P < 0.044; rs161976, OR = 1.594, P < 0.044; rs159497, OR = 1.445, P < 0.045; and rs9296092, OR = 1.832, P < 0.045) were risk factors for COPD, while 3 SNPs (rs8192288, OR = 0.593, P < 0.015; rs20541, OR = 0.669, P < 0.018; and rs12922394, OR = 0.651, P < 0.022) were protective factors for COPD development. In the training set, KNN, LR, SVM, DT and XGboost obtained AU-ROC values above 0.82 and AU-PRC values above 0.92. Among these models, XGboost obtained the highest AU-ROC (0.94), AU-PRC (0.97), accuracy (0.91), precision (0.95), F1 score (0.94), MCC (0.77) and specificity (0.85), while MLP obtained the highest sensitivity (recall) (0.99) and NPV (0.87). In the validation set, KNN, LR and XGboost obtained AU-ROC and AU-PRC values above 0.80 and 0.85, respectively. KNN had the highest precision (0.82), both KNN and LR obtained the same highest accuracy (0.81), and KNN and LR had the same highest F1 score (0.86). Both DT and MLP obtained sensitivity (recall) and NPV values above 0.94 and 0.84, respectively. In the feature importance analyses, we identified that AQCI, age, and BMI had the greatest impact on the predictive abilities of the models, while SNPs, sex and smoking were less important. CONCLUSIONS: The KNN, LR and XGboost models showed excellent overall predictive power, and the use of machine learning tools combining both clinical and SNP features was suitable for predicting the risk of COPD development.

Alteration of plasma metabolites associated with chemoradiosensitivity in esophageal squamous cell carcinoma via untargeted metabolomics approach.

BACKGROUND: To investigate the differences in plasma metabolomic characteristics between pathological complete response (pCR) and non-pCR patients and identify biomarker candidates for predicting the response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC). METHODS: A total of 46 ESCC patients were included in this study. Gas chromatography time-of- flight mass spectrometry (GC-TOF/MS) technology was applied to detect the plasma samples collected before nCRT via untargeted metabolomics analysis. RESULTS: Five differentially expressed metabolites (out of 109) was found in plasma between pCR and non-pCR groups. Compared with non-pCR group, isocitric acid (p = 0.0129), linoleic acid (p = 0.0137), citric acid (p = 0.0473) were upregulated, while L-histidine (p = 0.0155), 3'4 dihydroxyhydrocinnamic acid (p = 0.0339) were downregulated in the pCR plasma samples. Pathway analyses unveiled that citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolic pathway were associated with ESCC chemoradiosensitivity. CONCLUSION: The present study provided supporting evidence that GC-TOF/MS based metabolomics approach allowed identification of metabolite differences between pCR and non-pCR patients in plasma levels, and the systemic metabolic status of patients may reflect the response of ESCC patient to neoadjuvant chemoradiotherapy.

Comparative analysis of viral protein interaction networks in Hepatitis B virus and Hepatitis C virus infected HCC.

Previously, the different mechanisms of HBV infection and HCV infection were studied experimentally. Multiple studies also compared the differential network between HBV induced HCC and HCV induced HCC based on gene expression data. However network level comparison combining viral-human interaction network and dysfunctional protein interaction network for HBV and HCV-HCC has rarely been done before. In this work we did some pioneer job in construction of HBV/HCV viral dysfunctional network in HCC, in hope of investigating viral infection impact on the change of genome expression and eventually, the development of HCC. We found that HBx, the main HBV viral protein, directly acted on the gene groups of cell cycle, which could perfectly explain the dominant cell proliferation effect shown in the dysfunctional network of HBV-HCC. On the other hand, multiple important HCV viral proteins including CORE, NS3 and NS5A were found to target very important cancer related proteins such as TP53 and SMAD3, but no direct targeting to major immune response or inflammation related proteins. Therefore the dominant activation of immune response and inflammation related pathways shown in dysfunctional network of HCV-HCC might not be a direct effect of HCV infection. They might have been an indirect demonstration of activated cancer promoting pathways. Similar approaches may as well be applied to other important virus infection caused human diseases to help elucidate the mechanisms of virus-host interaction, and even help with investigations on anti-virus based therapies. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications.

dbDEPC 2.0: updated database of differentially expressed proteins in human cancers.

A large amount of differentially expressed proteins (DEPs) have been identified in various cancer proteomics experiments, curation and annotation of these proteins are important in deciphering their roles in oncogenesis and tumor progression, and may further help to discover potential protein biomarkers for clinical applications. In 2009, we published the first database of DEPs in human cancers (dbDEPCs). In this updated version of 2011, dbDEPC 2.0 has more than doubly expanded to over 4000 protein entries, curated from 331 experiments across 20 types of human cancers. This resource allows researchers to search whether their interested proteins have been reported changing in certain cancers, to compare their own proteomic discovery with previous studies, to picture selected protein expression heatmap across multiple cancers and to relate protein expression changes with aberrance in other genetic level. New important developments include addition of experiment design information, advanced filter tools for customer-specified analysis and a network analysis tool. We expect dbDEPC 2.0 to be a much more powerful tool than it was in its first release and can serve as reference to both proteomics and cancer researchers. dbDEPC 2.0 is available at http://lifecenter.sgst.cn/dbdepc/index.do.

Differential combinatorial regulatory network analysis related to venous metastasis of hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most fatal cancers in the world, and metastasis is a significant cause to the high mortality in patients with HCC. However, the molecular mechanism behind HCC metastasis is not fully understood. Study of regulatory networks may help investigate HCC metastasis in the way of systems biology profiling. METHODS: By utilizing both sequence information and parallel microRNA(miRNA) and mRNA expression data on the same cohort of HBV related HCC patients without or with venous metastasis, we constructed combinatorial regulatory networks of non-metastatic and metastatic HCC which contain transcription factor(TF) regulation and miRNA regulation. Differential regulation patterns, classifying marker modules, and key regulatory miRNAs were analyzed by comparing non-metastatic and metastatic networks. RESULTS: Globally TFs accounted for the main part of regulation while miRNAs for the minor part of regulation. However miRNAs displayed a more active role in the metastatic network than in the non-metastatic one. Seventeen differential regulatory modules discriminative of the metastatic status were identified as cumulative-module classifier, which could also distinguish survival time. MiR-16, miR-30a, Let-7e and miR-204 were identified as key miRNA regulators contributed to HCC metastasis. CONCLUSION: In this work we demonstrated an integrative approach to conduct differential combinatorial regulatory network analysis in the specific context venous metastasis of HBV-HCC. Our results proposed possible transcriptional regulatory patterns underlying the different metastatic subgroups of HCC. The workflow in this study can be applied in similar context of cancer research and could also be extended to other clinical topics.

Vitamin D and iodine status was associated with the risk and complication of type 2 diabetes mellitus in China.

The purpose of this study was to assess the relationship between 25-hydroxyvitamin D (25(OH)D), urinary iodine concentration (UIC), and type 2 diabetes mellitus (T2DM) risk and complications and to establish a model to predict T2DM in the general population. A total of 567 adults (389 T2DM patients and 178 controls) were enrolled, and the levels of 25(OH)D, iodine, and blood biochemical parameters were measured. Pearson's correlation analysis showed an inverse correlation between 25(OH)D level, UIC, and T2DM risk. Low 25(OH)D level was a risk factor for developing T2DM (OR, 0.81; 95% CI, 1.90-2.63; P = 0.043) after adjustment for multiple risk factors. 25(OH)D level and UIC were inversely correlated with short-term and long-term glucose levels. 25(OH)D deficiency was also associated with a high incidence of T2DM complicated with thyroid dysfunction. A prediction model based on 25(OH)D, iodine status, and other risk factors was established and recommended to screen high-risk T2DM in the general population and provide early screening and timely treatment for them.

Low-Density Lipoprotein Cholesterol 4: The Notable Risk Factor of Coronary Artery Disease Development.

Background: Coronary artery disease (CAD) is the leading cause of death worldwide, which has a long asymptomatic period of atherosclerosis. Thus, it is crucial to develop efficient strategies or biomarkers to assess the risk of CAD in asymptomatic individuals. Methods: A total of 356 consecutive CAD patients and 164 non-CAD controls diagnosed using coronary angiography were recruited. Blood lipids, other baseline characteristics, and clinical information were investigated in this study. In addition, low-density lipoprotein cholesterol (LDL-C) subfractions were classified and quantified using the Lipoprint system. Based on these data, we performed comprehensive analyses to investigate the risk factors for CAD development and to predict CAD risk. Results: Triglyceride, LDLC-3, LDLC-4, LDLC-5, LDLC-6, and total small and dense LDL-C were significantly higher in the CAD patients than those in the controls, whereas LDLC-1 and high-density lipoprotein cholesterol (HDL-C) had significantly lower levels in the CAD patients. Logistic regression analysis identified male [odds ratio (OR) = 2.875, P < 0.001], older age (OR = 1.018, P = 0.025), BMI (OR = 1.157, P < 0.001), smoking (OR = 4.554, P < 0.001), drinking (OR = 2.128, P < 0.016), hypertension (OR = 4.453, P < 0.001), and diabetes mellitus (OR = 8.776, P < 0.001) as clinical risk factors for CAD development. Among blood lipids, LDLC-3 (OR = 1.565, P < 0.001), LDLC-4 (OR = 3.566, P < 0.001), and LDLC-5 (OR = 6.866, P < 0.001) were identified as risk factors. To predict CAD risk, six machine learning models were constructed. The XGboost model showed the highest AUC score (0.945121), which could distinguish CAD patients from the controls with a high accuracy. LDLC-4 played the most important role in model construction. Conclusions: The established models showed good performance for CAD risk prediction, which can help screen high-risk CAD patients in asymptomatic population, so that further examination and prevention treatment might be taken before any sudden or serious event.

Recombinant human GLP-1 beinaglutide regulates lipid metabolism of adipose tissues in diet-induced obese mice.

GLP-1 analogs are a class of glucose-lowering agents with multiple benefits in diabetes, but its role in adipose tissues remains to be elucidated. The aim of this study was to determine the action of recombinant human GLP-1 (rhGLP-1) Beinaglutide (BN) in the insulin sensitivity and lipid metabolism of adipose tissues. We have shown that, after BN injection, obese mice displayed lower body weight, fat mass, and plasma lipid levels. In addition, BN promoted the insulin sensitivity in the white adipose tissues. Furthermore, we have found that the BN treatment caused significant changes in content and composition of different lipid classes, including glycerolipids, glycerophospholipids, and sphingolipids, as well as expression of genes in lipid metabolic pathways in the adipose tissues. Taken together, our data demonstrate that BN could resist HFD-induced obesity by targeting the composition of major lipid classes and the expression of genes in lipid metabolism of adipose tissues.

A preoperative risk model for early recurrence after radical resection may facilitate initial treatment decisions concerning the use of neoadjuvant therapy for patients with pancreatic ductal adenocarcinoma.

BACKGROUND: Neoadjuvant chemotherapy may benefit patients with pancreatic ductal adenocarcinoma with resectable and borderline disease. Inappropriate use of neoadjuvant therapy, however, may lead to the loss of therapeutic opportunities. Until an effective prediction model of individual drug sensitivity is established, no accurate model exists to help surgeons decide on the appropriate use of neoadjuvant chemotherapy. We hypothesized that early recurrence in patients undergoing upfront, early resection may be an indication for neoadjuvant chemotherapy. Therefore, we aimed to use preoperative clinical parameters to establish a model of early recurrence to select patients at high risk for neoadjuvant chemotherapy. METHODS: Patients who underwent resection for pancreatic ductal adenocarcinoma between January 2014 and November 2017 were analyzed retrospectively. After the minimum P-value approach, the patients were divided into three groups: early recurrence, middle recurrence, and late/non-recurrence. Preoperative clinicopathologic factors that could predict early recurrence were included in a Cox proportional hazards regression model for univariate and multivariate analyses. The factors related to early recurrence were included to establish nomogram and decision tree models, which were then validated in 68 patients. RESULTS: We found that 235 (72.5%) of 324 patients had recurrence with a median recurrence-free survival of 210 days. The early recurrence, middle recurrence, and late/non-recurrence groups differed in preoperative carbohydrate antigen 19-9 and carcinoembryonic antigen levels, "resectability" on cross-sectional imaging, resection requiring a vascular resection, T stage, tumor size, and adjuvant chemotherapy. The best cutoff value of early recurrence was the first 162 days postoperatively. Univariate and multivariate analyses showed that selected preoperative chief complaints, lymph node enlargement and resectability on cross-sectional imaging, preoperative carbohydrate antigen 19-9 levels >210 kU/L, and a neutrophil/lymphocyte ratio >4.2 were independent predictors for early recurrence. CONCLUSION: We have successfully built a prediction model of early recurrence of patients with pancreatic ductal adenocarcinoma with the optimal cutoff early-recurrence value of 162 days. Our nomogram and decision tree models may be used to select those at high risk for early recurrence to guide preoperative decision-making concerning the use of neoadjuvant therapy in those patients who have "resectable" disease and not only the more classic criteria of borderline resectability.