A BEST classification system of large to giant congenital melanocytic nevi based on expert consensus and distribution characteristics.

BACKGROUND: Large to giant congenital melanocytic nevi (LGCMN) significantly decrease patients' quality of life, but the inaccuracy of current classification system makes their clinical management challenging. OBJECTIVES: To improve and extend the existing LGCMN 6B/7B classification systems by developing a novel LGCMN classification system based on a new phenotypic approach to clinical tool development. METHODS: Three hundred and sixty-one LGCMN cases were categorized into four subtypes based on anatomic site: bonce (25.48%), extremity (17.73%), shawl (19.67%) and trunks (37.12%) LGCMN. A 'BEST' classification system of LGCMN was established and validated by a support vector machine classifier combined with the 7B system. RESULTS: The most common LGCMN distributions were on bonce and trunks (bathing trunk), whereas breast/belly and body LGCMN were exceptionally rare. Sexual dimorphism characterized distribution, with females showing a wider range of lesions in the genital area. Nearly half of the patients with bathing trunk LGCMN exhibited a butterfly-like distribution. Approximately half of the LGCMN with chest involvement did not have nipple-areola complex involvement. Abdomen, back and buttock involvement was associated with the presence of satellite nevi (r = 0.558), and back and buttock involvement was associated with the presence of nodules (r = 0.364). CONCLUSIONS: The effective quantification of a standardized anatomical site provides data support for the accuracy of the 6B/7B classification systems. The simplified BEST classification system can help establish a LGCMN clinical database for exploration of LGCMN aetiology, disease management and prognosis prediction.

Metabolic Characteristics and Discriminative Diagnosis of Growth Hormone Deficiency and Idiopathic Short Stature in Preadolescents and Adolescents.

Growth hormone deficiency (GHD) and idiopathic short stature (ISS) are the most common types of short stature (SS), but little is known about their pathogenesis, and even less is known about the study of adolescent SS. In this study, nuclear magnetic resonance (NMR)-based metabolomic analysis combined with least absolute shrinkage and selection operator (LASSO) were performed to identify the biomarkers of different types of SS (including 94 preadolescent GHD (PAG), 61 preadolescent ISS (PAI), 43 adolescent GHD (ADG), and 19 adolescent ISS (ADI)), and the receiver operating characteristic curve (ROC) was further used to evaluate the predictive power of potential biomarkers. The results showed that fourteen, eleven, nine, and fifteen metabolites were identified as the potential biomarkers of PAG, PAI, ADG, and ADI compared with their corresponding controls, respectively. The disturbed metabolic pathways in preadolescent SS were mainly carbohydrate metabolism and lipid metabolism, while disorders of amino acid metabolism played an important role in adolescent SS. The combination of aspartate, ethanolamine, phosphocholine, and trimethylamine was screened out to identify PAI from PAG, and alanine, histidine, isobutyrate, methanol, and phosphocholine gave a high classification accuracy for ADI and ADC. The differences in metabolic characteristics between GHD and ISS in preadolescents and adolescents will contribute to the development of individualized clinical treatments in short stature.

Accurate Classification and Prediction of Acute Myocardial Infarction through an ARMD Procedure.

The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose an ensemble machine learning analysis procedure named ADASYN-RFECV-MDA-DNN (ARMD) to address sample-unbalanced problems and enable stratification and prediction of AMI outcomes. The ARMD analysis procedure was applied to the NMR data of sera from 534 AMI-related subjects in four categories with an extremely imbalanced sample proportion. Firstly, the adaptive synthetic sampling (ADASYN) algorithm was used to address the issue of the original sample imbalance. Secondly, the recursive feature elimination with cross-validation (RFECV) processing and random forest mean decrease accuracy (RF-MDA) algorithm was performed to identify the differential metabolites corresponding to each AMI outcome. Finally, the deep neural network (DNN) was employed to classify and predict AMI events, and its performance was evaluated by comparing the four traditional machine learning methods. Compared with the other four machine learning models, DNN presented consistent superiority in almost all of the model parameters including precision, f1-score, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and classification accuracy, highlighting the potential of deep learning in classification and stratification of clinical diseases. The ARMD analysis procedure was a practical analysis tool for supervised classification and regression modeling of clinical diseases.

Metabolic characteristics and pathogenesis of precocious puberty in girls: the role of perfluorinated compounds.

BACKGROUND: Precocious puberty (PP) in girls is traditionally defined as the onset of breast development before the age of 8 years. The specific biomarkers of premature thelarche (PT) and central precocious puberty (CPP) girls are uncertain, and little is known about their metabolic characteristics driven by perfluorinated compounds (PFCs) and clinical phenotype. This study aimed to screen specific biomarkers of PT and CPP and elucidate their underlying pathogenesis. The relationships of clinical phenotype-serum PFCs-metabolic characteristics were also explored to reveal the relationship between PFCs and the occurrence and development of PT and CPP. METHODS: Nuclear magnetic resonance (NMR)-based cross-metabolomics strategy was performed on serum from 146 PP (including 30 CPP, 40 PT, and 76 unspecified PP) girls and 64 healthy girls (including 36 prepubertal and 28 adolescent). Specific biomarkers were screened by the uni- and multivariate statistical analyses. The relationships between serum PFCs and clinical phenotype were performed by correlation analysis and weighted gene co-expression network analysis to explore the link of clinical phenotype-PFCs-metabolic characteristics in PT and CPP. RESULTS: The disordered trend of pyruvate and butyrate metabolisms (metabolites mapped as formate, ethanol, and 3-hydroxybutyrate) were shared and kept almost consistent in PT and CPP. Eight and eleven specific biomarkers were screened for PT and CPP, respectively. The area under curve of specific biomarker combination was 0.721 in CPP vs. prepubertal, 0.972 in PT vs. prepubertal, 0.646 in CPP vs. prepubertal integrated adolescent, and 0.822 in PT vs. prepubertal integrated adolescent, respectively. Perfluoro-n-heptanoic acid and perfluoro-n-hexanoic acid were statistically different between PT and CPP. Estradiol and prolactin were significantly correlated with PFCs in CPP and PT. Clinical phenotypes and PFCs drive the metabolic characteristics and cause metabolic disturbances in CPP and PT. CONCLUSIONS: The elevation of formate, ethanol, and 3-hydroxybutyrate may serve as the early diagnostic indicator for PP in girls. But the stratification of PP still needs to be further determined based on the specific biomarkers. Specific biomarkers of CPP and PT exhibited good sensitivity and can facilitate the classification diagnosis of CPP and PT. PFC exposure is associated with endocrine homeostasis imbalance. PFC exposure and/or endocrine disturbance directly or indirectly drive metabolic changes and form overall metabolic network perturbations in CPP and PT.

Identification of potential biomarkers in malnutrition children with severity by 1H-NMR-based metabolomics: a preliminary study in the Chinese population.

PURPOSE: Child malnutrition is a global public health problem, but the underlying pathophysiologic mechanisms with severity remain poorly understood, and the potential biomarkers served to the clinical diagnosis are still not available. This study aimed to identify the serum metabolic characteristics of malnourished children with severity. METHODS: Fasted overnight serum samples were collected following clinical standard procedures among 275 malnourished and 199 healthy children from the Women and Children's Hospital, Xiamen University Child Health Department from July 2020 to May 2022. Nuclear magnetic resonance (NMR)-based metabolomics strategy was applied to identify the potential serum biomarkers of malnutrition from 275 malnourished children aged 4 to 84 months with mild (Mil, 199 cases), moderate (Mod, 101 cases), and severe (Sev, 7 cases) malnutrition. RESULTS: Ten, fifteen, and fifteen differential metabolites were identified from the Mil, Mod, and Sev malnutrition groups, respectively. Eight common metabolites, including increased acetoacetate, acetone, ethanol, succinate, 3-hydroxybutyrate, and decreased alanine, methionine, and N-acetyl-glycoprotein, could be the potential biomarkers for malnourished children. The altered metabolic pathways were mainly related to energy metabolism and amino acid metabolism via the network-based pathway enrichment. CONCLUSION: Eight potential biomarkers, including acetoacetate, acetone, ethanol, succinate, 3-hydroxybutyrate, alanine, methionine, and N-acetyl-glycoprotein, could characterize the child malnutrition. Child malnutrition-induced abnormal energy metabolism, impaired nutrition utilization and the reduced nutrient availability, and more metabolic disturbance will appear with the severity. Our results are valuable for further studies on the etiology and pathogenesis of malnutrition for clinical intervention and improvement.

Quantitative comparison and rapid discrimination of Panax notoginseng powder and Caulis clematidis armandii using NMR combined with pattern recognition.

BACKGROUND: The market demand for Panax notoginseng (P. notoginseng) is growing rapidly because of its useful properties in food and medicine. However, the frequent adulteration of P. notoginseng seriously affects the health of consumers and is a great challenge to food safety. In this study, low- and high-field nuclear magnetic resonance (LF/HF-NMR) were applied to detect the transverse relaxation distribution of P. notoginseng contaminated with different ratios of Caulis clematidis armandii (CCA) and the components in P. notoginseng and CCA, respectively. RESULTS: Fifty-seven kinds of major and minor components in P. notoginseng and CCA were identified and quantified from their high-resolution NMR spectra, and there were significant differences in ginsenosides, sucrose, and glucose between P. notoginseng and CCA. Furthermore, the partial least squares regression analysis results indicated that LF-NMR parameters (T21 and S21 ) changed linearly as the ratio of CCA increased, and these changes were attributed to the variations in polysaccharide and sucrose in adulterated P. notoginseng. CONCLUSION: In the relaxation time-based pattern recognition models, the authentic P. notoginseng powder could be classified with 100% accuracy from adulterated P. notoginseng when the adulteration ratio was greater than 30%, demonstrating the possibility of LF-NMR, in combination with pattern recognition, for rapid discrimination of food authenticity. © 2022 Society of Chemical Industry.

Metabolomic characteristics of hand-foot-mouth disease facilitate discovery and diagnosis of pathogeny.

BACKGROUND: Hand-foot-mouth disease (HFMD) is a significant public health concern, especially in Asia-Pacific countries. Its diagnosis mainly depends on clinical symptoms. It is easy to miss the source of infection and best treatment period. This research aims to provide a tool for its early clinical diagnosis and for predicting the possibility of complications. METHODS: The serum samples of 39 HFMD children and 36 healthy children were collected for clinical testing and 1 H-NMR spectroscopy. Metabolomic analyses were performed to obtain the metabolic differences between the HFMD and healthy children and to speculate on the pathogenesis of HFMD. RESULTS: Thirty-nine children were divided into severe cases and mild cases. Severe cases demonstrated more obvious inflammatory responses, but no metabolic difference was observed between the severe and mild cases. The metabolic differences between HFMD and healthy children were noticeable. Ten differential metabolites were screened out as the potential biomarkers for HFMD, and seven disturbed metabolic pathways responsible for HFMD were affected by inflammation, impaired intestinal absorptive function, and immune response. CONCLUSIONS: Our results will provide a complementary tool for the early diagnosis of HFMD and potential ideas for later treatment.

Compositional Analysis of Four Kinds of Citrus Fruits with an NMR-Based Method for Understanding Nutritional Value and Rational Utilization: From Pericarp to Juice.

Citrus is one of the most important economic crops and is widely distributed across the monsoon region. Citrus fruits are deeply loved by consumers because of their special color, fragrance and high nutritional value. However, their health benefits have not been fully understood, especially the pericarps of citrus fruits which have barely been utilized due to their unknown chemical composition. In the present study, the pericarp and juices of four typical varieties of citrus fruits (lemon, dekopon, sweet orange and pomelo) were analyzed by NMR spectroscopy combined with pattern recognition. A total of 62 components from the citrus juices and 87 components from the citrus pericarps were identified and quantified, respectively. The different varieties of the citrus fruits could be distinguished from the others, and the chemical markers in each citrus juice and pericarp were identified by a combination of univariate and multivariate statistical analyses. The nutritional analysis of citrus juices offers favorable diet recommendations for human consumption and data guidance for their potential medical use, and the nutritional analysis of citrus pericarps provides a data reference for the subsequent comprehensive utilization of citrus fruits. Our results not only provide an important reference for the potential nutritional and medical values of citrus fruits but also provide a feasible platform for the traceability analysis, adulteration identification and chemical composition analysis of other fruits.

Discriminant Analysis of the Nutritional Components between Organic Eggs and Conventional Eggs: A 1H NMR-Based Metabolomics Study.

The difference of nutrient composition between organic eggs and conventional eggs has always been a concern of people. In this study, 1H nuclear magnetic resonance (NMR) technique combined with multivariate statistical analyses was conducted to identify the metabolite different in egg yolk and egg white in order to reveal the nutritional components information between organic and conventional eggs. The results showed that the nutrient content and composition characteristics were different between organic and conventional eggs, among which the content of glucose, putrescine, amino acids and their derivatives were found higher in the organic eggs yolk, while phospholipids were demonstrated higher in conventional eggs yolk. Organic acid, alcohol, amine, choline and amino acids were higher in conventional eggs white, but glucose and lactate in organic egg were higher. Our study demonstrated that there are more nutritive components and higher nutritional value in organic eggs than conventional eggs, especially for the growth and development of infants and young children, and conventional eggs have more advantages in promoting lipid metabolism, preventing fatty liver, and reducing serum cholesterol. Eggs have important nutritional value to human body, and these two kinds of eggs can be selected according to the actual nutrient needs.

Metabolic Interactions and Differences between Coronary Heart Disease and Diabetes Mellitus: A Pilot Study on Biomarker Determination and Pathogenesis.

Comprehensive understanding of plasma metabotype of diabetes mellitus (DM), coronary heart disease (CHD), and especially diabetes mellitus with coronary heart disease (CHDDM) is still lacking. In this work, the plasma metabolic differences and links of DM, CHD, and CHDDM patients were investigated by the strategy of comparative metabolomics based on 1H NMR spectroscopy combined with network analysis for revealing their metabolic differences. A total of 17 metabolites are related to three diseases, among which valine, alanine, leucine, isoleucine, and N-acetyl-glycoprotein are positively correlated with CHD and CHDDM (odds ratios (OR) > 1). The trimethylamine oxide, glycerol, lactose, indoleacetate, and scyllo-inositol are closely related to the development of DM to CHDDM (OR > 1), and indoleactate (OR: 1.06, 95% confidence interval (CI): 1.01-1.12) and lactose (OR: 2.46, 95% CI: 1.67-3.25) are particularly prominent in CHDDM. We identified three multi-biomarkers types that were significantly associated with glycosylated hemoglobin (HbA1C) at baseline. All diseases demonstrated dysregulated glycolysis/gluconeogenesis and amino acid biosynthesis pathway. In addition, enrichment in tryptophan metabolism observed in CHDDM, enrichment in inositol phosphate metabolism observed in DM, and the metabolites related to microbiota metabolism were dysregulated in both DM and CHDDM. The comparative metabolomics strategy of multi-diseases offers a new perspective in disease-specific markers and pathogenic pathways.

Composition Profiling and Authenticity Assessment of Camellia Oil Using High Field and Low Field 1H NMR.

Camellia oil (CA), mainly produced in southern China, has always been called Oriental olive oil (OL) due to its similar physicochemical properties to OL. The high nutritional value and high selling price of CA make mixing it with other low-quality oils prevalent, in order to make huge profits. In this paper, the transverse relaxation time (T2) distribution of different brands of CA and OL, and the variation in transverse relaxation parameters when adulterated with corn oil (CO), were assessed via low field nuclear magnetic resonance (LF-NMR) imagery. The nutritional compositions of CA and OL and their quality indices were obtained via high field NMR (HF-NMR) spectroscopy. The results show that the fatty acid evaluation indices values, including for squalene, oleic acid, linolenic acid and iodine, were higher in CA than in OL, indicating the nutritional value of CA. The adulterated CA with a content of CO more than 20% can be correctly identified by principal component analysis or partial least squares discriminant analysis, and the blended oils could be successfully classified by orthogonal partial least squares discriminant analysis, with an accuracy of 100% when the adulteration ratio was above 30%. These results indicate the practicability of LF-NMR in the rapid screening of food authenticity.

Metabolomic analysis reveals metabolic characteristics of children with short stature caused by growth hormone deficiency.

The diagnosis of short stature (SS) is of widespread importance for later treatment. In the present paper, a metabolomic method was used to analyze the metabolic characteristics of SS children caused by endocrine metabolic diseases in order to understand the underlying biochemical mechanism and provide a potential intervention strategy for SS. According to the clinical diagnosis and family investigation, all patients with SS were confirmed to be due to the endocrine disorders, especially GH deficiency (GHD). A nuclear magnetic resonance (NMR)-based metabolomic analysis of serum was used to identify the metabolic changes in 45 SS children from the 35 healthy controls (HCs). The disturbed metabolic network related to SS was correspondingly derived from the differential metabolites. The SS children demonstrated higher serum levels of citrate, phenylalanine, creatinine, and tyrosine and lower serum levels of glucose, serine, betaine, inositol, lysine, glycerol, and glutamine compared with the HCs. The results demonstrated that the disturbed glucose metabolism and metabolism and biosynthesis of amino acids are typical metabolic features of SS, and the lower levels of lysine and glutamine are the metabolic characterization of the affected growth axes and stress state of SS, respectively. The significant changes of those serum metabolites are able to be regarded as potential biomarkers for the diagnosis of SS. Accordingly, supplemental betaine in dietary pattern, the improvement of glycometabolism, and endogenous replenishment of lysine and glutamine allow the possible treatment strategy for SS.

Metabolic Effect of Dietary Taurine Supplementation on Grouper (Epinephelus coioides): A 1H-NMR-Based Metabolomics Study.

Taurine is an indispensable amino acid for many fish species and taurine supplementation is needed when plant-based diets are used as the primary protein source for these species. However, there is limited information available to understand the physiological or metabolic effects of taurine on fish. In this study, 1H nuclear magnetic resonance (NMR)-based metabolomic analysis was conducted to identify the metabolic profile change in the fish intestine with the aim to assess the effect of dietary taurine supplementation on the physiological and metabolomic variation of fish, and reveal the possible mechanism of taurine's metabolic effect. Grouper (Epinephelus coioides) were divided into four groups and fed diets containing 0.0%, 0.5%, 1.0%, and 1.5% taurine supplementation for 84 days. After extraction using aqueous and organic solvents, 25 significant taurine-induced metabolic changes were identified. These metabolic changes in grouper intestine were characterized by differences in carbohydrate, amino acid, lipid and nucleotide. The results reflected both the physiological state and growth of the fish, and indicated that taurine supplementation significantly affects the metabolome of fish, improves energy utilization and amino acid uptake, promotes protein, lipid and purine synthesis, and accelerates fish growth.

Metabolic Response in Rabbit Urine to Occurrence and Relief of Unilateral Ureteral Obstruction.

Ureteral obstruction will lead clinically to hydronephrosis, which may further develop into partial or complete loss of kidney function and even cause permanent histological damage. However, there is little knowledge of metabolic responses during the obstructed process and its recoverability. In this study, a complete unilateral ureteral obstruction (CUUO) model was established in the rabbit, and 1H NMR-based metabolomic analysis of urine was used to reveal the metabolic perturbations in rabbits caused by CUUO and the metabolic recovery after the CUUO was relieved. Univariate and multivariate statistical analyses were used to identify metabolic characteristics. The gradually decreased levels of 3-hydroxykynurenine, 3-methylhistidine, creatinine, guanidoacetate, meta- and para-hydroxyphenylacetate, and phenylacetylglycine and the gradually increased levels of acetate, alanine, citrate, glycine, lactate, and methionine in urine could be regarded as potential biomarkers for the occurrence and severity of ureteral obstruction. And the reduced levels of 3-methylhistidine, creatinine, guanidoacetate, hippurate, meta-hydroxyphenylacetate, and methylguanidine and the elevated levels of 2-aminoisobutyrate, acetylcholine, citrate, lactate, lysine, valine, and α-ketoglutarate in urine compared with the obstructed level could characterize the metabolic recovery of ureteral obstruction. Our results depicted the disturbed biochemical pathways involved in ureteral obstruction and demonstrated the practicability of recovering renal functions for the patients with severe hydronephrosis in clinical practice by removing causes for obstruction.

[A novel metabolomic data scaling method based on K-L divergence].

A new scaling method in the current study based on Kullback-Leibler (K-L) divergence is proposed for NMR metabolomic data. The proposed method (called K-L scaling) is a supervised scaling method as group information is incorporated in the scaling procedure. Notably, K-L divergence measures the difference between two different datasets by their probability distributions, it can be used for the analysis of data that either follows Gaussian or non-Gaussian distributions. In K-L scaling, all variables were first standardized to unit variance, then their variance was adjusted using Kullback-Leibler divergence to highlight the significant variables. K-L scaling can tell effectively the difference in spectral data points between two experimental groups, and then enhances the weights of biological-relevant variables, and at the same time reduces the weight of noise and uninformative variables. The developed method was applied to a H-NMR metabolomic dataset acquired from human urine. Analysis results of the dataset showed that this new scaling method is efficient in suppressing the contribution of noise in the resulting multivariate model In addition, it can increase the weights of important variables, and improve the interpretability and predictability of subsequent principal component regression (PCR) and partial least squares discriminant analysis (PLS-DA). Furthermore, the scaling method facilitated the identification of metabolic signatures. The current result suggested that the developed K-L scaling method may become a useful alternative for the preprocessing of NMR-based metabolomic data.

Origin tracing and adulteration identification of bird's nest by high- and low-field NMR combined with pattern recognition.

Edible bird's nest (EBN) is a high-value health food with various nutrients and bioactive components. With increasing demand for EBN, they are often adulterated with cheaper ingredients or falsely labeled by the origin information, thus harming consumer interests. In this study, high- and low-field nuclear magnetic resonance (HF/LF-NMR) technology combined with multivariate statistical analysis was used to identify the geographical marker of EBN from different origins and authenticate the adulterated EBN with various adulterants at different adulteration rates. Authentic EBN samples from Malaysia were used to simulate adulteration using gelatin (GL), agar (AG) and starch (ST) at 10 %, 20 %, 40 %, 60 %, 80 %, and 100 % w/w, respectively. The results showed significant differences in composition among EBN from different origins, with isocaproate and citric acid serving as geographical markers for Malaysia and Vietnam, respectively. Leucine, glutamic acid, and N-acetylglycoprotein serving as geographical markers for Indonesia. In addition, PLS model further verified the accuracy of origin identification of EBN. The LF-NMR results of adulteration EBN showed a linear correlation between the transverse relaxation (T2, S2) and the adulterated ratio. The OPLS-DA based on T2 spectra could accurately identify authentic EBN from adulterated with GL, AG and ST at 40 %, 20 %, and 20 %, respectively. Fisher discrimination model was able to differentiate at 20 %, 20 %, and 40 %, respectively. These results show that the 1H NMR combined with multivariate statistical analysis method could be a potential tool for the detection of origin and adulteration of EBN.

Childhood overweight and obesity: age stratification contributes to the differences in metabolic characteristics.

OBJECTIVE: The aim of this study was to identify the differential metabolic characteristics of children with overweight and obesity and understand their potential mechanism in different age stratifications. METHODS: Four hundred seventy-three children were recruited and divided into two age stratifications: >4 years (older children) and ≤4 years (younger children), and overweight and obesity were defined according to their BMI percentile. A one dimensional proton nuclear magnetic resonance (1 H-NMR)-based metabolomics strategy combined with pattern recognition methods was used to identify the metabolic characteristics of childhood overweight and obesity. RESULTS: Four and sixteen potential biomarkers related to overweight and two and twenty potential biomarkers related to obesity were identified from younger and older children, respectively. Fluctuations in phenylalanine, tyrosine, glutamine, leucine, histidine, and ascorbate co-occurred in children with obesity at two age stratifications. The disturbances in biosynthesis and metabolism of amino acids, lipid metabolism, and galactose metabolism disturbance were mainly involved in children with overweight and obesity. CONCLUSIONS: The metabolic disturbances show a significant progression from overweight to obesity in children, and different metabolic characteristics were demonstrated in age stratifications. The changes in the levels of phenylalanine, tyrosine, glutamine, leucine, histidine, and ascorbate were tracked with the persistence of childhood obesity. These findings will promote the mechanistic understanding of childhood overweight and obesity.

Metabolomic analysis reveals the biological characteristics of giant congenital melanocytic nevi.

Giant congenital melanocytic nevi (GCMN) is a congenital cutaneous developmental deformity tumor that usually occurs at birth or in the first few weeks after birth, but its pathogenesis is still unclear. In this study, nuclear magnetic resonance-based metabolomics strategy was employed to evaluate the metabolic variations in serum and urine of the GCMN patients in order to understand its underlying biochemical mechanism and provide a potential intervention idea. Twenty-nine metabolites were observed to change significantly in serum and urine metabolomes, which are mainly involved in a variety of metabolic pathways including glyoxylate and dicarboxylate metabolism, TCA cycle and metabolisms of amino acids. The substantial cores of all the disturbed metabolic pathways are related to amino acid metabolism and carbohydrate metabolism and regulate the physiological state of the GCMN patients. Our results provide the physiological basis and physiological responses of GCMN and will be helpful for better understanding the molecular mechanisms of GCMN in future research.

Integration of metabolomics and network pharmacology for enhancing mechanism understanding and medication combination recommendation for diabetes mellitus and diabetic nephropathy.

With the increasing prevalence of diabetes mellitus (DM) and diabetic nephropathy (DN), effective treatment is particularly important for the recovery of patients. However, the currently approved drugs are usually tailored to clinical symptoms and no mechanism-targeted drugs are available. In this study, the combination of metabolomics and network pharmacology was applied to provide reasonable medication combination regimens to meet the different clinical needs for the targeted treatment of DM and DN. An NMR-based metabolomic strategy was applied to identify the potential urinary biomarkers of DM or/and DN, while network pharmacology was used to identify the therapy targets of DM and DN by intersecting the targets of diseases and currently approved drugs. According to the enriched signaling pathways using the potential biomarkers and the therapy targets, the specific medication combinations were recommended for the specific clinical demands in terms of hypoglycemic, hypertensive, and/or lipid-lowering. For DM, 17 potential urinary biomarkers and 12 disease-related signaling pathways were identified, and 34 combined medication regimens related to hypoglycemia, hypoglycemia, and hypertension, and hypoglycemia, hypertension, and lipid-lowering were administered. For DN, 22 potential urinary biomarkers and 12 disease-related signaling pathways were identified, and 21 combined medication regimens related to hypoglycemia, hypoglycemia, and hypertension were proposed. Molecular docking was used to verify the binding ability, docking sites, and structure of the drug molecules to target proteins. Moreover, an integrated biological information network of the drug-target-metabolite-signaling pathways was constructed to provide insights into the underlined mechanism of DM and DN as well as clinical combination therapy.

Discover boy specific-biomarkers and reveal gender-related metabolic differences in central precocious puberty.

The incidence of central precocious puberty (CPP) in boys is rising, but lack of effective molecular biomarkers often leads to delayed treatment and thus the terrible clinical complications in adulthood. This study aims to identify the specific-biomarkers of CPP boys and understand the gender-related differences in metabolic characteristics of CPP. The specific-biomarkers of CPP boys were identified from serum by cross-metabolomics combined with linear discriminant analysis effect size analysis after age correction, and union receiver operating characteristic curve analyses were perform to optimize the combination of specific-biomarkers. The differences in metabolic characteristics between boys and girls with CPP were explored by cross-metabolomics and weighted gene co-expression network analysis. Results show that CPP activated in advance the HPG axis and induced gender-related clinical phenotypes. Seven serum metabolites were identified as specific-biomarkers of CPP boys, including acetoacetate, aspartate, choline, creatinine, myo-inositol, N,N-dimethylglycine and N-Acetyl-glycoprotein. The combination of aspartate, choline, myo-inositol and creatinine achieved an optimized diagnosis, where AUC is 0.949, prediction accuracy for CPP boys is 91.1%, and the average accuracy is 0.865. The metabolic disorders of CPP boys mainly involve in glycerophospholipid metabolism, and synthesis and degradation of ketone bodies. Betaine, glutamine, isoleucine, lactate, leucine, lysine, pyruvate, α-&ß-glucose were identified as gender-related biomarkers for CPP, and they are mainly involved in glycolysis/gluconeogenesis, pyruvate metabolism, and alanine, aspartate and glutamate metabolism. Biomarkers combination provides a promising diagnostic potential for CPP boy with a favorite sensitivity and specificity. In addition, the differences of metabolic characteristics between boys and girls with CPP will contribute to the development of individualized clinical treatments in CPP.