[Risk prediction of metabolic syndrome and coronary artery disease in overweight and obese populations based on serum metabolomics].

Objective: By identifying different metabolites in the serum and clarifying the potential metabolic disorder pathways in metabolic syndrome (MS) and stable coronary artery disease patients, to evaluate the predictive value of specific metabolites based on serum metabolomics for the occurrence of MS and coronary heart disease in overweight or obese populations. Methods: This is a retrospective cross-sectional study. Patients with Metabolic Syndrome (MS group), patients with stable coronary heart disease (coronary heart disease group), and overweight or obese individuals (control group) recruited from the Central District of the First Affiliated Hospital of Zhengzhou University from 2017 to 2019 were assigned to the training set, meanwhile, the corresponding three groups of people recruited from the East District of the hospital during the same period were assigned to the validation test. The serum metabolomics profiles were determined by ultra-performance liquid chromatography-quadrupole/orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS). Clinical characteristics (age, gender, body mass index (BMI), blood pressure, fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c), alanine aminotransferase (ALT), aspartate transaminase (AST), total cholesterol (TC), triacylglycerol (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), glomerular filtration rate (eGFR), creatinine (CR)) were also collected. Based on the orthogonal partial least-squares discrimination analysis (OPLS-DA) model, the significantly changed metabolites for MS and coronary artery disease patients were screened according to variable important in projection (VIP), and the receiver operating characteristic (ROC) analysis was evaluated for the risk prediction values of changed metabolites. Results: A total of 488 subjects were recruited in this study, the training set included 40 MS, 249 coronary artery disease patients and 148 controls, the validation set included 16 MS, 18 coronary artery disease patients and 17 controls. We made comparisons of the serum metabolites of coronary artery disease vs. controls, MS vs. controls, and coronary artery disease vs. MS, and a total of 22 different metabolites were identified. The disturbed metabolic pathways involved were phospholipid metabolism, amino acid metabolism, purine metabolism and other pathways. Through cross-comparisons, we identified 2 specific metabolites for MS (phosphatidylcholine (18∶1(9Z)e/20) and pipecolic acid), 4 specific metabolites for coronary artery disease (lysophosphatidylcholine (17∶0), PC(16∶0/16∶0), hypoxanthine and histidine), and 4 common metabolites both for MS and coronary artery disease (isoleucine, phenylalanine, glutathione and LysoPC(14∶0)). Based on the cut-off values from ROC curve, the predictive value of the above metabolites for the occurrence of MS in overweight or obese populations is 100%, the predictive value for the occurrence of coronary heart disease is 87.5%, and the risk predictive value for coronary heart disease in MS patients is 82.1%. Conclusions: The altered serum metabolites suggest that MS and coronary heart disease may involve multiple metabolic pathway disorders. Specific metabolites based on serum metabolomics have good predictive value for the occurrence of MS and coronary heart disease in overweight or obese populations.

[Establishment of an auxiliary diagnosis system of newborn screening for inherited metabolic diseases based on artificial intelligence technology and a clinical trial].

Objective: To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology. Methods: This was a retrospectively study. Newborn screening data (n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data (n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results: A total of 3 665 697 newborns' screening data were collected including 3 019 cases' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment (n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion: An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.